dataset_name stringlengths 8 32 | series_description stringlengths 1.32k 2.25k | algorithm stringclasses 8 values | hyperparameters stringclasses 93 values |
|---|---|---|---|
3001-61.csv | A multivariate classification time-series dataset consists of 216 samples and 2 features with 2 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 216 samples the target ground-truth class has changed 2 times representing a percentage of 0.97%. There are 2 features in the dataset
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 2 numerical features using the dickey-fuller test and the rest are Unstationary. 0 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.177. to 0.250 and kurtosis values of 1.89 to 1.91. The fractal dimension analysis yields values ranging from -0.85 to -0.82 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.95, maximum 1.00, mean 0.97, and standard deviation 0.03. The count of numerical predictors with outliers is 0 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=3), 'learning_rate': 0.1, 'n_estimators': 200} |
1030-415-classification.csv | A multivariate classification time-series dataset consists of 3415 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.36 showing a Unbalanced dataset. Among the 3415 samples the target ground-truth class has changed 14 times representing a percentage of 0.41%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 1.323. to 10.445 and kurtosis values of 0.60 to 247.60. The fractal dimension analysis yields values ranging from -0.65 to -0.35 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.01, maximum 1.00, mean 0.68, and standard deviation 0.47. The count of numerical predictors with outliers is 5 with the minimum percentage of 3.76%, maximum percentage of 5.00%, average percentage of 4.44%, and standard deviation percentage of 0.45%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1030-120-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 49 times representing a percentage of 1.19%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.367. to 2.854 and kurtosis values of 0.67 to 19.27. The fractal dimension analysis yields values ranging from -0.60 to -0.34 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.35, maximum 1.00, mean 0.57, and standard deviation 0.61. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 4.54%, average percentage of 0.91%, and standard deviation percentage of 2.03%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
3001-4.csv | A multivariate classification time-series dataset consists of 168 samples and 2 features with 2 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 168 samples the target ground-truth class has changed 1 times representing a percentage of 0.63%. There are 2 features in the dataset
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 2 numerical features using the dickey-fuller test and the rest are Unstationary. 0 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.080. to 0.083 and kurtosis values of 1.96 to 1.96. The fractal dimension analysis yields values ranging from -0.85 to -0.84 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.95, maximum 1.00, mean 0.97, and standard deviation 0.03. The count of numerical predictors with outliers is 0 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-4-3-1-2-classification.csv | A multivariate classification time-series dataset consists of 7648 samples and 16 features with 15 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.40 showing a Unbalanced dataset. Among the 7648 samples the target ground-truth class has changed 1422 times representing a percentage of 18.68%. There are 16 features in the dataset with a ratio of numerical to categorical features of 15.0.
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 12 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.053. to 1.709 and kurtosis values of 0.29 to 4.07. The fractal dimension analysis yields values ranging from -0.62 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.84, maximum 1.00, mean 0.10, and standard deviation 0.57. The count of numerical predictors with outliers is 13 with the minimum percentage of 0.00%, maximum percentage of 13.36%, average percentage of 5.81%, and standard deviation percentage of 5.49%.
Among the categorical predictors, the count of symbols ranges from 99 to 99 with a minimum entropy value 0.44091310791143196, maximum entropy 0.44091310791143196, mean 0.44091310791143196, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-58-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 6777 samples and 14 features with 13 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 3054 to 3054 with mean 3054.0
The target column has 3 classes with entropy value 1.34 showing a Unbalanced dataset. Among the 6777 samples the target ground-truth class has changed 627 times representing a percentage of 16.88%. There are 14 features in the dataset with a ratio of numerical to categorical features of 13.0.
Among the numerical predictors, the series has 13 numerical features detected as Stationary out of the 13 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.476. to 1.061 and kurtosis values of 0.69 to 1.66. The fractal dimension analysis yields values ranging from -0.45 to -0.37 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.79, maximum 1.00, mean 0.96, and standard deviation 0.07. The count of numerical predictors with outliers is 0 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%, and standard deviation percentage of 0.00%.
Among the categorical predictors, the count of symbols ranges from 2 to 2 with a minimum entropy value 0.9929833790411436, maximum entropy 0.9929833790411436, mean 0.9929833790411436, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-40-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7488 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.42 showing a Unbalanced dataset. Among the 7488 samples the target ground-truth class has changed 1020 times representing a percentage of 13.68%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.096. to 0.890 and kurtosis values of 0.01 to 0.59. The fractal dimension analysis yields values ranging from -0.27 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.64, maximum 1.00, mean 0.91, and standard deviation 0.11. The count of numerical predictors with outliers is 16 with the minimum percentage of 16.11%, maximum percentage of 45.92%, average percentage of 22.04%, and standard deviation percentage of 11.85%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1016-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 7385 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 18 to 18 with mean 18.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7385 samples the target ground-truth class has changed 191 times representing a percentage of 2.60%. There are 8 features in the dataset with a ratio of numerical to categorical features of 1.0.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 3 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.065. to 0.531 and kurtosis values of 0.10 to 0.53. The fractal dimension analysis yields values ranging from -0.46 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.11, maximum 1.00, mean 0.66, and standard deviation 0.42. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.04%, maximum percentage of 1.00%, average percentage of 0.29%, and standard deviation percentage of 0.47%.
Among the categorical predictors, the count of symbols ranges from 40 to 67 with a minimum entropy value 1.447941497794789, maximum entropy 5.230659831466964, mean 4.110866132522819, and standard deviation 1.5446926496819071,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-40-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 6860 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 6860 samples the target ground-truth class has changed 1534 times representing a percentage of 22.47%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.026. to 16.683 and kurtosis values of 0.06 to 467.86. The fractal dimension analysis yields values ranging from -0.65 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.09, and standard deviation 0.51. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.91%, maximum percentage of 16.73%, average percentage of 6.90%, and standard deviation percentage of 4.32%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-30-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 6058 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.43 showing a Unbalanced dataset. Among the 6058 samples the target ground-truth class has changed 1267 times representing a percentage of 21.03%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.032. to 11.884 and kurtosis values of 0.03 to 183.57. The fractal dimension analysis yields values ranging from -0.63 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.10, and standard deviation 0.47. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.72%, maximum percentage of 17.25%, average percentage of 10.80%, and standard deviation percentage of 4.51%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1021-3-classification.csv | A multivariate classification time-series dataset consists of 7012 samples and 7 features with 4 numerical and 3 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 246 with mean 123.00 and standard deviation 142.03. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.45 showing a Unbalanced dataset. Among the 7012 samples the target ground-truth class has changed 665 times representing a percentage of 9.53%. There are 7 features in the dataset with a ratio of numerical to categorical features of 1.3333333333333333.
Among the numerical predictors, the series has 3 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 29 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 389,876,1168 The numerical predictors also exhibit skewness values ranging from 0.429. to 4.254 and kurtosis values of 0.57 to 22.66. The fractal dimension analysis yields values ranging from -0.37 to -0.14 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.84, maximum 1.00, mean 0.12, and standard deviation 0.71. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 10.30%, average percentage of 2.58%, and standard deviation percentage of 5.15%.
Among the categorical predictors, the count of symbols ranges from 5 to 26 with a minimum entropy value 0.08499178964251711, maximum entropy 2.05072071833909, mean 0.8601679760939582, and standard deviation 0.8545664856545357,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 40, 'n_estimators': 200} |
1020-37-4-classification.csv | A multivariate classification time-series dataset consists of 7012 samples and 11 features with 9 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.43 showing a Unbalanced dataset. Among the 7012 samples the target ground-truth class has changed 553 times representing a percentage of 7.92%. There are 11 features in the dataset with a ratio of numerical to categorical features of 4.5.
Among the numerical predictors, the series has 8 numerical features detected as Stationary out of the 9 numerical features using the dickey-fuller test and the rest are Unstationary. 9 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 26 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 584,637,1753 The numerical predictors also exhibit skewness values ranging from 0.405. to 3.244 and kurtosis values of 0.40 to 13.23. The fractal dimension analysis yields values ranging from -0.73 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.77, maximum 1.00, mean 0.15, and standard deviation 0.46. The count of numerical predictors with outliers is 7 with the minimum percentage of 0.00%, maximum percentage of 11.34%, average percentage of 4.34%, and standard deviation percentage of 3.78%.
Among the categorical predictors, the count of symbols ranges from 17 to 75 with a minimum entropy value 0.5518450227716487, maximum entropy 3.9392277468431582, mean 2.2455363848074033, and standard deviation 1.6936913620357548,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1016-4-2-4-classification.csv | A multivariate classification time-series dataset consists of 7109 samples and 12 features with 4 numerical and 8 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 7109 samples the target ground-truth class has changed 333 times representing a percentage of 4.71%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.5.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 6 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 284,309,418 The numerical predictors also exhibit skewness values ranging from 0.061. to 0.459 and kurtosis values of 0.08 to 0.50. The fractal dimension analysis yields values ranging from -0.50 to -0.07 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.03, maximum 1.00, mean 0.61, and standard deviation 0.48. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 1.00%, average percentage of 0.53%, and standard deviation percentage of 0.42%.
Among the categorical predictors, the count of symbols ranges from 9 to 60 with a minimum entropy value 1.584909357253086, maximum entropy 5.104181997368391, mean 3.6849579496875995, and standard deviation 1.4105305853964138,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.00055, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-53-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 6726 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.36 showing a Unbalanced dataset. Among the 6726 samples the target ground-truth class has changed 808 times representing a percentage of 12.07%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.008. to 23.574 and kurtosis values of 1.18 to 695.31. The fractal dimension analysis yields values ranging from -0.70 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.89, maximum 1.00, mean 0.09, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.09%, maximum percentage of 41.93%, average percentage of 38.78%, and standard deviation percentage of 9.89%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1030-137-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 62 times representing a percentage of 1.50%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.749. to 2.566 and kurtosis values of 1.14 to 13.04. The fractal dimension analysis yields values ranging from -0.65 to -0.33 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.53, maximum 1.00, mean 0.51, and standard deviation 0.71. The count of numerical predictors with outliers is 5 with the minimum percentage of 3.13%, maximum percentage of 4.12%, average percentage of 3.57%, and standard deviation percentage of 0.37%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 0.1, 'n_estimators': 50} |
3001-79.csv | A multivariate classification time-series dataset consists of 504 samples and 1 features with 1 numerical and 0 categorical features. Each instance has a window length of 3. The dataset has a sampling rate of 480.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00
The target column has 4 classes with entropy value 1.27 showing a Unbalanced dataset. Among the 504 samples the target ground-truth class has changed 329 times representing a percentage of 66.87%. There are 1 features in the dataset
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 1 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.855. to 0.855 and kurtosis values of 0.81 to 0.81. The fractal dimension analysis yields values ranging from -1.38 to -1.38 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 1.00, maximum 1.00, mean 1.00, and standard deviation 0.00. The count of numerical predictors with outliers is 0 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 5, 'n_estimators': 200} |
1031-44-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 7536 samples and 15 features with 15 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 7536 samples the target ground-truth class has changed 539 times representing a percentage of 7.18%. There are 15 features in the dataset
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.978. to 5.929 and kurtosis values of 3.25 to 36.53. The fractal dimension analysis yields values ranging from -0.62 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.15, maximum 1.00, mean 0.88, and standard deviation 0.22. The count of numerical predictors with outliers is 15 with the minimum percentage of 22.50%, maximum percentage of 22.50%, average percentage of 22.50%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-31-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7517 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.36 showing a Unbalanced dataset. Among the 7517 samples the target ground-truth class has changed 1176 times representing a percentage of 15.72%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.019. to 11.073 and kurtosis values of 0.00 to 159.10. The fractal dimension analysis yields values ranging from -0.65 to -0.14 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.95, maximum 1.00, mean 0.11, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 4.53%, maximum percentage of 16.46%, average percentage of 9.21%, and standard deviation percentage of 3.63%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1030-96-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 25 times representing a percentage of 0.61%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.518. to 9.485 and kurtosis values of 0.59 to 222.04. The fractal dimension analysis yields values ranging from -0.62 to -0.33 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.32, maximum 1.00, mean 0.58, and standard deviation 0.61. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.41%, average percentage of 1.08%, and standard deviation percentage of 2.42%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-61-1-3-classification.csv | A multivariate classification time-series dataset consists of 7668 samples and 13 features with 13 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 3812 with mean 293.23 and standard deviation 1057.26.
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7668 samples the target ground-truth class has changed 1078 times representing a percentage of 14.12%. There are 13 features in the dataset
Among the numerical predictors, the series has 13 numerical features detected as Stationary out of the 13 numerical features using the dickey-fuller test and the rest are Unstationary. 10 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.016. to 22.831 and kurtosis values of 1.03 to 647.02. The fractal dimension analysis yields values ranging from -0.71 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.13, and standard deviation 0.54. The count of numerical predictors with outliers is 13 with the minimum percentage of 21.10%, maximum percentage of 44.85%, average percentage of 41.92%, and standard deviation percentage of 7.14%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1031-26-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7440 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7440 samples the target ground-truth class has changed 1257 times representing a percentage of 16.97%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.065. to 11.376 and kurtosis values of 0.07 to 167.11. The fractal dimension analysis yields values ranging from -0.66 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.92, maximum 1.00, mean 0.12, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.07%, maximum percentage of 9.55%, average percentage of 6.49%, and standard deviation percentage of 2.44%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 100.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
3001-80.csv | A multivariate classification time-series dataset consists of 288 samples and 1 features with 1 numerical and 0 categorical features. Each instance has a window length of 4. The dataset has a sampling rate of 360.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 1 to 1 with mean 1.00
The target column has 3 classes with entropy value 1.50 showing a Unbalanced dataset. Among the 288 samples the target ground-truth class has changed 274 times representing a percentage of 99.64%. There are 1 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 1 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.955. to 0.955 and kurtosis values of 0.18 to 0.18. The fractal dimension analysis yields values ranging from -1.24 to -1.24 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 1.00, maximum 1.00, mean 1.00, and standard deviation 0.00. The count of numerical predictors with outliers is 0 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=3), 'learning_rate': 0.5, 'n_estimators': 50} |
3001-23.csv | A multivariate classification time-series dataset consists of 144 samples and 1 features with 1 numerical and 0 categorical features. Each instance has a window length of 3. The dataset has a sampling rate of 480.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00
The target column has 2 classes with entropy value 0.92 showing a Unbalanced dataset. Among the 144 samples the target ground-truth class has changed 88 times representing a percentage of 66.67%. There are 1 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 1 numerical features using the dickey-fuller test and the rest are Unstationary. 0 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.011. to 0.011 and kurtosis values of 1.40 to 1.40. The fractal dimension analysis yields values ranging from -1.46 to -1.46 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 1.00, maximum 1.00, mean 1.00, and standard deviation 0.00. The count of numerical predictors with outliers is 0 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 100} |
1030-229-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 23 times representing a percentage of 0.56%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.394. to 14.861 and kurtosis values of 1.30 to 495.54. The fractal dimension analysis yields values ranging from -0.63 to -0.32 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.02, maximum 1.00, mean 0.69, and standard deviation 0.45. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.36%, average percentage of 1.07%, and standard deviation percentage of 2.40%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
3001-52.csv | A multivariate classification time-series dataset consists of 240 samples and 1 features with 1 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00
The target column has 2 classes with entropy value 0.98 showing a Balanced dataset. Among the 240 samples the target ground-truth class has changed 2 times representing a percentage of 0.90%. There are 1 features in the dataset
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 1 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 1.245. to 1.245 and kurtosis values of 0.50 to 0.50. The fractal dimension analysis yields values ranging from -0.52 to -0.52 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 1.00, maximum 1.00, mean 1.00, and standard deviation 0.00. The count of numerical predictors with outliers is 1 with the minimum percentage of 6.28%, maximum percentage of 6.28%, average percentage of 6.28%,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=3), 'learning_rate': 0.01, 'n_estimators': 150} |
1031-44-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7997 samples and 16 features with 15 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 7997 samples the target ground-truth class has changed 267 times representing a percentage of 3.35%. There are 16 features in the dataset with a ratio of numerical to categorical features of 15.0.
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.045. to 3.029 and kurtosis values of 4.60 to 22.71. The fractal dimension analysis yields values ranging from -0.61 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.96, maximum 1.00, mean 0.14, and standard deviation 0.49. The count of numerical predictors with outliers is 15 with the minimum percentage of 23.11%, maximum percentage of 23.11%, average percentage of 23.11%, and standard deviation percentage of 0.00%.
Among the categorical predictors, the count of symbols ranges from 34 to 34 with a minimum entropy value 0.11193283694221748, maximum entropy 0.11193283694221748, mean 0.11193283694221748, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1030-340-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.52 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 26 times representing a percentage of 0.63%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.929. to 4.462 and kurtosis values of 0.39 to 40.22. The fractal dimension analysis yields values ranging from -0.58 to -0.32 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.28, maximum 1.00, mean 0.59, and standard deviation 0.59. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.90%, maximum percentage of 5.67%, average percentage of 2.01%, and standard deviation percentage of 2.05%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1016-19-2-3-classification.csv | A multivariate classification time-series dataset consists of 7210 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 18 to 18 with mean 18.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 7210 samples the target ground-truth class has changed 264 times representing a percentage of 3.69%. There are 8 features in the dataset with a ratio of numerical to categorical features of 1.0.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 2 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.140. to 0.352 and kurtosis values of 0.21 to 0.73. The fractal dimension analysis yields values ranging from -0.41 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.00, maximum 1.00, mean 0.62, and standard deviation 0.47. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 1.69%, average percentage of 0.42%, and standard deviation percentage of 0.85%.
Among the categorical predictors, the count of symbols ranges from 29 to 67 with a minimum entropy value 1.4502335612551125, maximum entropy 5.228933735352424, mean 3.9108772011233284, and standard deviation 1.4916860759128816,
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 12190.718728749973, 'penalty': 'l1', 'solver': 'saga'} |
1030-483-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 5 times representing a percentage of 0.12%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.650. to 3.648 and kurtosis values of 0.91 to 20.94. The fractal dimension analysis yields values ranging from -0.58 to -0.31 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.31, maximum 1.00, mean 0.58, and standard deviation 0.61. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 6.09%, average percentage of 1.22%, and standard deviation percentage of 2.72%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1034-3-3-classification.csv | A multivariate classification time-series dataset consists of 7963 samples and 6 features with 5 numerical and 1 categorical features. Each instance has a window length of 4. The dataset has a sampling rate of 15.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 2 classes with entropy value 0.98 showing a Balanced dataset. Among the 7963 samples the target ground-truth class has changed 237 times representing a percentage of 2.98%. There are 6 features in the dataset with a ratio of numerical to categorical features of 5.0.
Among the numerical predictors, the series has 5 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.580. to 1.519 and kurtosis values of 0.06 to 1.88. The fractal dimension analysis yields values ranging from -0.71 to -0.36 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.31, maximum 1.00, mean 0.31, and standard deviation 0.54. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.44%, maximum percentage of 11.33%, average percentage of 5.19%, and standard deviation percentage of 5.55%.
Among the categorical predictors, the count of symbols ranges from 64 to 64 with a minimum entropy value 5.186860625261318, maximum entropy 5.186860625261318, mean 5.186860625261318, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
3001-66.csv | A multivariate classification time-series dataset consists of 648 samples and 3 features with 3 numerical and 0 categorical features. Each instance has a window length of 3. The dataset has a sampling rate of 480.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 648 samples the target ground-truth class has changed 2 times representing a percentage of 0.31%. There are 3 features in the dataset
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 3 numerical features using the dickey-fuller test and the rest are Unstationary. 2 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.265. to 2.023 and kurtosis values of 1.86 to 3.63. The fractal dimension analysis yields values ranging from -1.16 to -0.89 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.33, maximum 1.00, mean 0.59, and standard deviation 0.29. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 11.64%, average percentage of 6.66%, and standard deviation percentage of 6.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=3), 'learning_rate': 0.1, 'n_estimators': 50} |
1031-39-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 6752 samples and 16 features with 15 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 6752 samples the target ground-truth class has changed 1142 times representing a percentage of 17.00%. There are 16 features in the dataset with a ratio of numerical to categorical features of 15.0.
Among the numerical predictors, the series has 12 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 12 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.035. to 1.265 and kurtosis values of 0.09 to 5.66. The fractal dimension analysis yields values ranging from -0.56 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.78, maximum 1.00, mean 0.10, and standard deviation 0.55. The count of numerical predictors with outliers is 15 with the minimum percentage of 12.62%, maximum percentage of 35.34%, average percentage of 23.85%, and standard deviation percentage of 6.88%.
Among the categorical predictors, the count of symbols ranges from 57 to 57 with a minimum entropy value 0.2510740255473868, maximum entropy 0.2510740255473868, mean 0.2510740255473868, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1016-11-3-1-classification.csv | A multivariate classification time-series dataset consists of 7110 samples and 12 features with 4 numerical and 8 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 7110 samples the target ground-truth class has changed 316 times representing a percentage of 4.47%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.5.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.155. to 0.473 and kurtosis values of 0.01 to 0.67. The fractal dimension analysis yields values ranging from -0.43 to -0.10 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.17, maximum 1.00, mean 0.69, and standard deviation 0.38. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.01%, maximum percentage of 1.29%, average percentage of 0.46%, and standard deviation percentage of 0.60%.
Among the categorical predictors, the count of symbols ranges from 9 to 71 with a minimum entropy value 1.2822112647640953, maximum entropy 5.4103542764391905, mean 3.7001904685188554, and standard deviation 1.493058249568962,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.00055, 'penalty': 'elasticnet', 'solver': 'saga'} |
1020-50-3-classification.csv | A multivariate classification time-series dataset consists of 7012 samples and 11 features with 9 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.44 showing a Unbalanced dataset. Among the 7012 samples the target ground-truth class has changed 874 times representing a percentage of 12.53%. There are 11 features in the dataset with a ratio of numerical to categorical features of 4.5.
Among the numerical predictors, the series has 8 numerical features detected as Stationary out of the 9 numerical features using the dickey-fuller test and the rest are Unstationary. 9 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 60 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 1753,2337,2337 The numerical predictors also exhibit skewness values ranging from 0.326. to 4.109 and kurtosis values of 0.23 to 23.11. The fractal dimension analysis yields values ranging from -0.65 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.81, maximum 1.00, mean 0.13, and standard deviation 0.47. The count of numerical predictors with outliers is 8 with the minimum percentage of 0.00%, maximum percentage of 10.42%, average percentage of 3.81%, and standard deviation percentage of 3.58%.
Among the categorical predictors, the count of symbols ranges from 17 to 70 with a minimum entropy value 0.5913848919242175, maximum entropy 3.871875942227257, mean 2.231630417075737, and standard deviation 1.6402455251515198,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1031-46-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7402 samples and 16 features with 15 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.36 showing a Unbalanced dataset. Among the 7402 samples the target ground-truth class has changed 748 times representing a percentage of 10.15%. There are 16 features in the dataset with a ratio of numerical to categorical features of 15.0.
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 11 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.027. to 1.447 and kurtosis values of 1.25 to 8.80. The fractal dimension analysis yields values ranging from -0.48 to -0.10 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.92, maximum 1.00, mean 0.13, and standard deviation 0.55. The count of numerical predictors with outliers is 15 with the minimum percentage of 38.65%, maximum percentage of 38.65%, average percentage of 38.65%, and standard deviation percentage of 0.00%.
Among the categorical predictors, the count of symbols ranges from 23 to 23 with a minimum entropy value 0.10951783662202137, maximum entropy 0.10951783662202137, mean 0.10951783662202137, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 99999.99999999999, 'penalty': 'l1', 'solver': 'saga'} |
1031-11-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 6631 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.45 showing a Unbalanced dataset. Among the 6631 samples the target ground-truth class has changed 1262 times representing a percentage of 19.13%. There are 16 features in the dataset
Among the numerical predictors, the series has 13 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.046. to 14.684 and kurtosis values of 0.08 to 249.24. The fractal dimension analysis yields values ranging from -0.62 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.86, maximum 1.00, mean 0.09, and standard deviation 0.51. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.32%, maximum percentage of 14.57%, average percentage of 8.21%, and standard deviation percentage of 4.84%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 100.0, 'l1_ratio': 0.001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-29-2-1-6-classification.csv | A multivariate classification time-series dataset consists of 7301 samples and 13 features with 13 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7301 samples the target ground-truth class has changed 1158 times representing a percentage of 15.94%. There are 13 features in the dataset
Among the numerical predictors, the series has 7 numerical features detected as Stationary out of the 13 numerical features using the dickey-fuller test and the rest are Unstationary. 11 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.002. to 14.940 and kurtosis values of 0.55 to 287.27. The fractal dimension analysis yields values ranging from -0.59 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.33, maximum 1.00, mean 0.30, and standard deviation 0.46. The count of numerical predictors with outliers is 13 with the minimum percentage of 2.66%, maximum percentage of 47.27%, average percentage of 30.85%, and standard deviation percentage of 16.35%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1016-13-5-4-classification.csv | A multivariate classification time-series dataset consists of 6594 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.51 showing a Unbalanced dataset. Among the 6594 samples the target ground-truth class has changed 449 times representing a percentage of 6.84%. There are 8 features in the dataset with a ratio of numerical to categorical features of 1.0.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 10 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 299,329,599 The numerical predictors also exhibit skewness values ranging from 0.241. to 0.728 and kurtosis values of 0.07 to 0.60. The fractal dimension analysis yields values ranging from -0.42 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.28, maximum 1.00, mean 0.73, and standard deviation 0.33. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 1.72%, average percentage of 0.76%, and standard deviation percentage of 0.89%.
Among the categorical predictors, the count of symbols ranges from 37 to 54 with a minimum entropy value 1.371172390426648, maximum entropy 5.190194140612123, mean 4.048443359106481, and standard deviation 1.55376616729617,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-46-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7298 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.43 showing a Unbalanced dataset. Among the 7298 samples the target ground-truth class has changed 1362 times representing a percentage of 18.75%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.009. to 17.939 and kurtosis values of 0.09 to 406.02. The fractal dimension analysis yields values ranging from -0.65 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.89, maximum 1.00, mean 0.10, and standard deviation 0.51. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 15.06%, average percentage of 6.71%, and standard deviation percentage of 4.95%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-24-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 7548 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 7548 samples the target ground-truth class has changed 462 times representing a percentage of 6.15%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 1.276. to 11.018 and kurtosis values of 3.22 to 122.76. The fractal dimension analysis yields values ranging from -0.22 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.21, maximum 1.00, mean 0.88, and standard deviation 0.24. The count of numerical predictors with outliers is 16 with the minimum percentage of 24.19%, maximum percentage of 24.19%, average percentage of 24.19%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 0.1, 'n_estimators': 50} |
1031-49-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 6836 samples and 13 features with 13 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.48 showing a Unbalanced dataset. Among the 6836 samples the target ground-truth class has changed 1576 times representing a percentage of 23.17%. There are 13 features in the dataset
Among the numerical predictors, the series has 13 numerical features detected as Stationary out of the 13 numerical features using the dickey-fuller test and the rest are Unstationary. 11 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.027. to 15.058 and kurtosis values of 0.09 to 293.07. The fractal dimension analysis yields values ranging from -0.62 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.90, maximum 1.00, mean 0.15, and standard deviation 0.52. The count of numerical predictors with outliers is 13 with the minimum percentage of 0.44%, maximum percentage of 13.79%, average percentage of 6.15%, and standard deviation percentage of 4.24%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-7-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 6729 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.45 showing a Unbalanced dataset. Among the 6729 samples the target ground-truth class has changed 1271 times representing a percentage of 18.98%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.028. to 19.958 and kurtosis values of 0.10 to 509.46. The fractal dimension analysis yields values ranging from -0.63 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.95, maximum 1.00, mean 0.10, and standard deviation 0.52. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.91%, maximum percentage of 12.77%, average percentage of 6.16%, and standard deviation percentage of 4.09%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-5-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 7546 samples and 16 features with 15 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 7546 samples the target ground-truth class has changed 1466 times representing a percentage of 19.52%. There are 16 features in the dataset with a ratio of numerical to categorical features of 15.0.
Among the numerical predictors, the series has 14 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.118. to 1.359 and kurtosis values of 0.05 to 3.93. The fractal dimension analysis yields values ranging from -0.61 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.90, maximum 1.00, mean 0.09, and standard deviation 0.55. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.07%, maximum percentage of 15.15%, average percentage of 7.09%, and standard deviation percentage of 5.30%.
Among the categorical predictors, the count of symbols ranges from 110 to 110 with a minimum entropy value 0.5260484252201842, maximum entropy 0.5260484252201842, mean 0.5260484252201842, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1030-418-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 4140 samples the target ground-truth class has changed 25 times representing a percentage of 0.61%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.566. to 4.282 and kurtosis values of 0.09 to 37.90. The fractal dimension analysis yields values ranging from -0.59 to -0.33 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.38, maximum 1.00, mean 0.56, and standard deviation 0.64. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 5.62%, average percentage of 1.16%, and standard deviation percentage of 2.50%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1030-246-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.58 showing a Balanced dataset. Among the 4140 samples the target ground-truth class has changed 14 times representing a percentage of 0.34%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.579. to 34.011 and kurtosis values of 0.77 to 1725.02. The fractal dimension analysis yields values ranging from -0.66 to -0.34 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.02, maximum 1.00, mean 0.69, and standard deviation 0.45. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 4.63%, average percentage of 0.93%, and standard deviation percentage of 2.07%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 0.1, 'n_estimators': 50} |
1016-2-3-4-classification.csv | A multivariate classification time-series dataset consists of 7385 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 7385 samples the target ground-truth class has changed 224 times representing a percentage of 3.05%. There are 8 features in the dataset with a ratio of numerical to categorical features of 1.0.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 3 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 1 seasonality components detected in the numerical predictors. The top 1 common seasonality components are represented using sinusoidal waves. of periods 189 The numerical predictors also exhibit skewness values ranging from 0.056. to 0.492 and kurtosis values of 0.29 to 0.55. The fractal dimension analysis yields values ranging from -0.45 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.15, maximum 1.00, mean 0.68, and standard deviation 0.39. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 1.43%, average percentage of 0.43%, and standard deviation percentage of 0.67%.
Among the categorical predictors, the count of symbols ranges from 36 to 62 with a minimum entropy value 1.470065729972895, maximum entropy 5.338651962942513, mean 4.15102266919532, and standard deviation 1.5588954538315976,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-25-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7707 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 7707 samples the target ground-truth class has changed 437 times representing a percentage of 5.70%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.710. to 19.270 and kurtosis values of 2.25 to 372.61. The fractal dimension analysis yields values ranging from -0.51 to -0.10 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.11, maximum 1.00, mean 0.87, and standard deviation 0.28. The count of numerical predictors with outliers is 16 with the minimum percentage of 23.45%, maximum percentage of 23.45%, average percentage of 23.45%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-53-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 5547 samples and 12 features with 12 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.36 showing a Unbalanced dataset. Among the 5547 samples the target ground-truth class has changed 763 times representing a percentage of 13.84%. There are 12 features in the dataset
Among the numerical predictors, the series has 12 numerical features detected as Stationary out of the 12 numerical features using the dickey-fuller test and the rest are Unstationary. 9 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.015. to 13.840 and kurtosis values of 0.54 to 252.09. The fractal dimension analysis yields values ranging from -0.49 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.95, maximum 1.00, mean 0.14, and standard deviation 0.54. The count of numerical predictors with outliers is 12 with the minimum percentage of 17.74%, maximum percentage of 45.89%, average percentage of 36.21%, and standard deviation percentage of 8.51%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 181.8181818181818, 'l1_ratio': 0.00055, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-23-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7599 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 7599 samples the target ground-truth class has changed 229 times representing a percentage of 3.03%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.436. to 31.210 and kurtosis values of 4.25 to 1061.36. The fractal dimension analysis yields values ranging from -0.55 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.06, maximum 1.00, mean 0.84, and standard deviation 0.31. The count of numerical predictors with outliers is 16 with the minimum percentage of 15.24%, maximum percentage of 15.24%, average percentage of 15.24%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 4728.708045015879, 'penalty': 'l1', 'solver': 'saga'} |
1031-21-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 6816 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.44 showing a Unbalanced dataset. Among the 6816 samples the target ground-truth class has changed 835 times representing a percentage of 12.31%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.066. to 14.194 and kurtosis values of 0.06 to 291.92. The fractal dimension analysis yields values ranging from -0.72 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.96, maximum 1.00, mean 0.11, and standard deviation 0.45. The count of numerical predictors with outliers is 16 with the minimum percentage of 4.04%, maximum percentage of 20.72%, average percentage of 12.07%, and standard deviation percentage of 5.81%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-5-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 7551 samples and 16 features with 15 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.39 showing a Unbalanced dataset. Among the 7551 samples the target ground-truth class has changed 1293 times representing a percentage of 17.20%. There are 16 features in the dataset with a ratio of numerical to categorical features of 15.0.
Among the numerical predictors, the series has 12 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.169. to 1.516 and kurtosis values of 0.03 to 4.30. The fractal dimension analysis yields values ranging from -0.62 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.86, maximum 1.00, mean 0.10, and standard deviation 0.54. The count of numerical predictors with outliers is 15 with the minimum percentage of 2.51%, maximum percentage of 20.11%, average percentage of 11.03%, and standard deviation percentage of 4.56%.
Among the categorical predictors, the count of symbols ranges from 102 to 102 with a minimum entropy value 0.44814253854288727, maximum entropy 0.44814253854288727, mean 0.44814253854288727, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1029-20-classification.csv | A multivariate classification time-series dataset consists of 3503 samples and 4 features with 4 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.41 showing a Unbalanced dataset. Among the 3503 samples the target ground-truth class has changed 25 times representing a percentage of 0.72%. There are 4 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 1.083. to 2.258 and kurtosis values of 1.00 to 9.02. The fractal dimension analysis yields values ranging from -0.63 to -0.32 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.52, maximum 1.00, mean 0.43, and standard deviation 0.73. The count of numerical predictors with outliers is 4 with the minimum percentage of 4.50%, maximum percentage of 7.48%, average percentage of 6.72%, and standard deviation percentage of 1.48%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1016-9-2-1-classification.csv | A multivariate classification time-series dataset consists of 7110 samples and 12 features with 4 numerical and 8 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 7110 samples the target ground-truth class has changed 322 times representing a percentage of 4.55%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.5.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.253. to 0.599 and kurtosis values of 0.02 to 0.64. The fractal dimension analysis yields values ranging from -0.44 to -0.10 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.13, maximum 1.00, mean 0.67, and standard deviation 0.40. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 0.61%, average percentage of 0.18%, and standard deviation percentage of 0.29%.
Among the categorical predictors, the count of symbols ranges from 9 to 70 with a minimum entropy value 0.8723659437299113, maximum entropy 5.259918767728648, mean 3.6168200911210864, and standard deviation 1.4689153815180813,
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 99999.99999999999, 'penalty': 'l1', 'solver': 'saga'} |
1020-37-2-classification.csv | A multivariate classification time-series dataset consists of 7010 samples and 11 features with 9 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.44 showing a Unbalanced dataset. Among the 7010 samples the target ground-truth class has changed 529 times representing a percentage of 7.58%. There are 11 features in the dataset with a ratio of numerical to categorical features of 4.5.
Among the numerical predictors, the series has 8 numerical features detected as Stationary out of the 9 numerical features using the dickey-fuller test and the rest are Unstationary. 9 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 23 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 304,318,637 The numerical predictors also exhibit skewness values ranging from 0.239. to 2.936 and kurtosis values of 0.43 to 10.39. The fractal dimension analysis yields values ranging from -0.76 to -0.10 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.78, maximum 1.00, mean 0.16, and standard deviation 0.46. The count of numerical predictors with outliers is 9 with the minimum percentage of 0.01%, maximum percentage of 9.52%, average percentage of 3.45%, and standard deviation percentage of 3.24%.
Among the categorical predictors, the count of symbols ranges from 17 to 57 with a minimum entropy value 0.39640719398225427, maximum entropy 3.945389457094151, mean 2.170898325538203, and standard deviation 1.7744911315559484,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
3001-10.csv | A multivariate classification time-series dataset consists of 168 samples and 1 features with 1 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00
The target column has 3 classes with entropy value 1.27 showing a Unbalanced dataset. Among the 168 samples the target ground-truth class has changed 43 times representing a percentage of 28.48%. There are 1 features in the dataset
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 1 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 1 seasonality components detected in the numerical predictors. The top 1 common seasonality components are represented using sinusoidal waves. of periods 7 The numerical predictors also exhibit skewness values ranging from 0.929. to 0.929 and kurtosis values of 0.51 to 0.51. The fractal dimension analysis yields values ranging from -0.61 to -0.61 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 1.00, maximum 1.00, mean 1.00, and standard deviation 0.00. The count of numerical predictors with outliers is 0 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=1), 'learning_rate': 0.01, 'n_estimators': 50} |
1031-34-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7315 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.47 showing a Unbalanced dataset. Among the 7315 samples the target ground-truth class has changed 1637 times representing a percentage of 22.48%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.045. to 14.426 and kurtosis values of 0.15 to 301.67. The fractal dimension analysis yields values ranging from -0.68 to -0.14 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.96, maximum 1.00, mean 0.10, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.08%, maximum percentage of 13.50%, average percentage of 6.35%, and standard deviation percentage of 4.55%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1016-11-4-5-classification.csv | A multivariate classification time-series dataset consists of 7109 samples and 12 features with 6 numerical and 6 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 16 with mean 2.67 and standard deviation 6.53. Similarly, the missing values percentages for categorical features range from 16 to 16 with mean 16.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7109 samples the target ground-truth class has changed 286 times representing a percentage of 4.05%. There are 12 features in the dataset with a ratio of numerical to categorical features of 1.0.
Among the numerical predictors, the series has 6 numerical features detected as Stationary out of the 6 numerical features using the dickey-fuller test and the rest are Unstationary. 6 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 3 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 32,105,128 The numerical predictors also exhibit skewness values ranging from 0.000. to 0.988 and kurtosis values of 0.13 to 0.98. The fractal dimension analysis yields values ranging from -0.54 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.37, maximum 1.00, mean 0.31, and standard deviation 0.51. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 2.05%, average percentage of 0.65%, and standard deviation percentage of 0.90%.
Among the categorical predictors, the count of symbols ranges from 13 to 65 with a minimum entropy value 1.7142221907697435, maximum entropy 5.025634954296156, mean 3.387650588190024, and standard deviation 1.4053040311214908,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
2003.csv | A multivariate classification time-series dataset consists of 876 samples and 10 features with 5 numerical and 5 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 4 classes with entropy value 1.72 showing a Unbalanced dataset. Among the 876 samples the target ground-truth class has changed 557 times representing a percentage of 64.69%. There are 10 features in the dataset with a ratio of numerical to categorical features of 1.0.
Among the numerical predictors, the series has 5 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 11 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 62,175,438 The numerical predictors also exhibit skewness values ranging from 0.010. to 1.430 and kurtosis values of 0.08 to 1.26. The fractal dimension analysis yields values ranging from -0.91 to -0.30 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.36, maximum 1.00, mean 0.28, and standard deviation 0.45. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 21.25%, average percentage of 4.67%, and standard deviation percentage of 9.30%.
Among the categorical predictors, the count of symbols ranges from 2 to 7 with a minimum entropy value 0.2711541430007089, maximum entropy 2.8072323119341904, mean 1.363433961946632, and standard deviation 0.9151653923936545,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 10, 'reg_lambda': 0.2} |
1030-438-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.55 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 32 times representing a percentage of 0.78%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.390. to 28.530 and kurtosis values of 0.38 to 1107.56. The fractal dimension analysis yields values ranging from -0.64 to -0.31 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.16, maximum 1.00, mean 0.63, and standard deviation 0.54. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.34%, average percentage of 1.07%, and standard deviation percentage of 2.39%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1016-24-5-1-classification.csv | A multivariate classification time-series dataset consists of 7110 samples and 12 features with 4 numerical and 8 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7110 samples the target ground-truth class has changed 224 times representing a percentage of 3.17%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.5.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 3 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.085. to 0.885 and kurtosis values of 0.02 to 1.24. The fractal dimension analysis yields values ranging from -0.43 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.14, maximum 1.00, mean 0.56, and standard deviation 0.54. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 2.56%, average percentage of 1.24%, and standard deviation percentage of 1.06%.
Among the categorical predictors, the count of symbols ranges from 9 to 71 with a minimum entropy value 1.164166369000331, maximum entropy 5.388656168560601, mean 3.6251132825781998, and standard deviation 1.4840949958119813,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-14-2-1-6-classification.csv | A multivariate classification time-series dataset consists of 7440 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 7440 samples the target ground-truth class has changed 1614 times representing a percentage of 21.79%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.053. to 16.695 and kurtosis values of 0.14 to 365.34. The fractal dimension analysis yields values ranging from -0.71 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.90, maximum 1.00, mean 0.08, and standard deviation 0.50. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 13.91%, average percentage of 6.06%, and standard deviation percentage of 4.08%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-45-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7479 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 5541 with mean 346.31 and standard deviation 1385.25.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 7479 samples the target ground-truth class has changed 314 times representing a percentage of 4.22%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.082. to 4.487 and kurtosis values of 4.11 to 57.57. The fractal dimension analysis yields values ranging from -0.62 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.60, maximum 1.00, mean 0.18, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 18.44%, maximum percentage of 22.78%, average percentage of 22.51%, and standard deviation percentage of 1.08%.
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 99999.99999999999, 'penalty': 'l1', 'solver': 'saga'} |
1031-88-1-2-classification.csv | A multivariate classification time-series dataset consists of 7803 samples and 16 features with 15 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7803 samples the target ground-truth class has changed 1434 times representing a percentage of 18.46%. There are 16 features in the dataset with a ratio of numerical to categorical features of 15.0.
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.059. to 1.069 and kurtosis values of 0.05 to 2.88. The fractal dimension analysis yields values ranging from -0.64 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.88, maximum 1.00, mean 0.11, and standard deviation 0.53. The count of numerical predictors with outliers is 15 with the minimum percentage of 11.62%, maximum percentage of 27.22%, average percentage of 21.17%, and standard deviation percentage of 5.67%.
Among the categorical predictors, the count of symbols ranges from 98 to 98 with a minimum entropy value 0.44817073255127937, maximum entropy 0.44817073255127937, mean 0.44817073255127937, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1019-1-classification.csv | A multivariate classification time-series dataset consists of 1100 samples and 17 features with 16 numerical and 1 categorical features. Each instance has a window length of 4. The dataset has a sampling rate of 15.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 1100 samples the target ground-truth class has changed 22 times representing a percentage of 2.03%. There are 17 features in the dataset with a ratio of numerical to categorical features of 16.0.
Among the numerical predictors, the series has 14 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.144. to 4.965 and kurtosis values of 0.11 to 38.34. The fractal dimension analysis yields values ranging from -0.66 to -0.00 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.66, maximum 1.00, mean 0.26, and standard deviation 0.44. The count of numerical predictors with outliers is 7 with the minimum percentage of 0.00%, maximum percentage of 16.02%, average percentage of 3.26%, and standard deviation percentage of 5.73%.
Among the categorical predictors, the count of symbols ranges from 6 to 6 with a minimum entropy value 0.1005636453394341, maximum entropy 0.1005636453394341, mean 0.1005636453394341, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 4.101480898093607, 'penalty': 'l1', 'solver': 'saga'} |
1031-39-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7227 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.41 showing a Unbalanced dataset. Among the 7227 samples the target ground-truth class has changed 1303 times representing a percentage of 18.11%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 12 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.023. to 14.951 and kurtosis values of 0.17 to 262.00. The fractal dimension analysis yields values ranging from -0.67 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.87, maximum 1.00, mean 0.09, and standard deviation 0.53. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.04%, maximum percentage of 27.55%, average percentage of 10.94%, and standard deviation percentage of 6.33%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1016-19-3-5-classification.csv | A multivariate classification time-series dataset consists of 7209 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 7209 samples the target ground-truth class has changed 195 times representing a percentage of 2.72%. There are 8 features in the dataset with a ratio of numerical to categorical features of 1.0.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 3 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.067. to 0.358 and kurtosis values of 0.26 to 0.93. The fractal dimension analysis yields values ranging from -0.37 to -0.07 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.10, maximum 1.00, mean 0.66, and standard deviation 0.42. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 1.44%, average percentage of 0.36%, and standard deviation percentage of 0.72%.
Among the categorical predictors, the count of symbols ranges from 29 to 54 with a minimum entropy value 1.4479447930596896, maximum entropy 5.196806761807025, mean 3.856360414664283, and standard deviation 1.4402241844999089,
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 43093.49564030827, 'penalty': 'l1', 'solver': 'saga'} |
1031-5-3-1-3-classification.csv | A multivariate classification time-series dataset consists of 7333 samples and 15 features with 15 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.36 showing a Unbalanced dataset. Among the 7333 samples the target ground-truth class has changed 1109 times representing a percentage of 15.19%. There are 15 features in the dataset
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 12 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.023. to 1.320 and kurtosis values of 0.04 to 3.53. The fractal dimension analysis yields values ranging from -0.58 to -0.10 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.87, maximum 1.00, mean 0.12, and standard deviation 0.53. The count of numerical predictors with outliers is 15 with the minimum percentage of 6.58%, maximum percentage of 29.43%, average percentage of 19.93%, and standard deviation percentage of 6.37%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 100.0, 'l1_ratio': 0.00055, 'penalty': 'elasticnet', 'solver': 'saga'} |
1016-24-5-2-classification.csv | A multivariate classification time-series dataset consists of 7109 samples and 12 features with 5 numerical and 7 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 7 to 7 with mean 7.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7109 samples the target ground-truth class has changed 357 times representing a percentage of 5.05%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.7142857142857143.
Among the numerical predictors, the series has 5 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 10 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 394,443,1183 The numerical predictors also exhibit skewness values ranging from 0.096. to 1.749 and kurtosis values of 0.16 to 1.15. The fractal dimension analysis yields values ranging from -0.65 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.30, maximum 1.00, mean 0.34, and standard deviation 0.57. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.50%, maximum percentage of 16.99%, average percentage of 4.37%, and standard deviation percentage of 7.07%.
Among the categorical predictors, the count of symbols ranges from 13 to 70 with a minimum entropy value 1.498698388943018, maximum entropy 5.306418539496347, mean 3.4030451768004903, and standard deviation 1.4476995783486903,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-26-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 7593 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.40 showing a Unbalanced dataset. Among the 7593 samples the target ground-truth class has changed 1304 times representing a percentage of 17.25%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.024. to 10.112 and kurtosis values of 0.01 to 130.80. The fractal dimension analysis yields values ranging from -0.70 to -0.15 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.92, maximum 1.00, mean 0.12, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.36%, maximum percentage of 10.57%, average percentage of 5.34%, and standard deviation percentage of 2.91%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
3001-22.csv | A multivariate classification time-series dataset consists of 480 samples and 2 features with 2 numerical and 0 categorical features. Each instance has a window length of 4. The dataset has a sampling rate of 360.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.33 showing a Unbalanced dataset. Among the 480 samples the target ground-truth class has changed 352 times representing a percentage of 75.54%. There are 2 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 2 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.057. to 1.222 and kurtosis values of 0.22 to 0.68. The fractal dimension analysis yields values ranging from -1.20 to -1.14 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.34, maximum 1.00, mean 0.67, and standard deviation 0.33. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.43%, maximum percentage of 3.22%, average percentage of 1.82%, and standard deviation percentage of 1.97%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=5), 'learning_rate': 1.0, 'n_estimators': 150} |
1031-33-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 7469 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.43 showing a Unbalanced dataset. Among the 7469 samples the target ground-truth class has changed 1447 times representing a percentage of 19.46%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.010. to 14.643 and kurtosis values of 0.29 to 310.45. The fractal dimension analysis yields values ranging from -0.64 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.90, maximum 1.00, mean 0.08, and standard deviation 0.48. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 14.59%, average percentage of 6.46%, and standard deviation percentage of 4.05%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1016-4-3-4-classification.csv | A multivariate classification time-series dataset consists of 7109 samples and 12 features with 4 numerical and 8 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 7109 samples the target ground-truth class has changed 320 times representing a percentage of 4.52%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.5.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 6 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 169,273,507 The numerical predictors also exhibit skewness values ranging from 0.153. to 0.533 and kurtosis values of 0.25 to 0.82. The fractal dimension analysis yields values ranging from -0.50 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.01, maximum 1.00, mean 0.62, and standard deviation 0.46. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 1.85%, average percentage of 0.64%, and standard deviation percentage of 0.82%.
Among the categorical predictors, the count of symbols ranges from 9 to 59 with a minimum entropy value 1.3506576501946088, maximum entropy 5.199892287732872, mean 3.709215799336399, and standard deviation 1.423487938965127,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 100.0, 'l1_ratio': 0.001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-47-2-1-6-classification.csv | A multivariate classification time-series dataset consists of 7403 samples and 15 features with 15 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7403 samples the target ground-truth class has changed 678 times representing a percentage of 9.20%. There are 15 features in the dataset
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.020. to 1.058 and kurtosis values of 0.06 to 1.04. The fractal dimension analysis yields values ranging from -0.64 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.75, maximum 1.00, mean 0.15, and standard deviation 0.52. The count of numerical predictors with outliers is 11 with the minimum percentage of 0.00%, maximum percentage of 7.15%, average percentage of 0.66%, and standard deviation percentage of 1.82%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-33-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 7189 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 7189 samples the target ground-truth class has changed 1395 times representing a percentage of 19.50%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.026. to 14.020 and kurtosis values of 0.03 to 244.80. The fractal dimension analysis yields values ranging from -0.68 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.88, maximum 1.00, mean 0.09, and standard deviation 0.48. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 13.40%, average percentage of 5.79%, and standard deviation percentage of 3.86%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1030-264-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.52 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 34 times representing a percentage of 0.82%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.298. to 8.754 and kurtosis values of 0.30 to 208.63. The fractal dimension analysis yields values ranging from -0.62 to -0.32 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.23, maximum 1.00, mean 0.61, and standard deviation 0.57. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.15%, maximum percentage of 5.87%, average percentage of 1.46%, and standard deviation percentage of 2.47%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-32-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7550 samples and 14 features with 14 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.43 showing a Unbalanced dataset. Among the 7550 samples the target ground-truth class has changed 1386 times representing a percentage of 18.44%. There are 14 features in the dataset
Among the numerical predictors, the series has 11 numerical features detected as Stationary out of the 14 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.030. to 1.863 and kurtosis values of 0.06 to 4.56. The fractal dimension analysis yields values ranging from -0.52 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.90, maximum 1.00, mean 0.13, and standard deviation 0.57. The count of numerical predictors with outliers is 14 with the minimum percentage of 1.42%, maximum percentage of 13.15%, average percentage of 8.52%, and standard deviation percentage of 3.46%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-17-2-1-6-classification.csv | A multivariate classification time-series dataset consists of 7401 samples and 13 features with 12 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7401 samples the target ground-truth class has changed 1200 times representing a percentage of 16.29%. There are 13 features in the dataset with a ratio of numerical to categorical features of 12.0.
Among the numerical predictors, the series has 12 numerical features detected as Stationary out of the 12 numerical features using the dickey-fuller test and the rest are Unstationary. 12 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.097. to 1.298 and kurtosis values of 0.24 to 2.26. The fractal dimension analysis yields values ranging from -0.27 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.84, maximum 1.00, mean 0.16, and standard deviation 0.60. The count of numerical predictors with outliers is 12 with the minimum percentage of 10.82%, maximum percentage of 21.49%, average percentage of 16.25%, and standard deviation percentage of 3.76%.
Among the categorical predictors, the count of symbols ranges from 56 to 56 with a minimum entropy value 0.4095866734482979, maximum entropy 0.4095866734482979, mean 0.4095866734482979, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-31-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7477 samples and 15 features with 7 numerical and 8 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 7477 samples the target ground-truth class has changed 1029 times representing a percentage of 13.83%. There are 15 features in the dataset with a ratio of numerical to categorical features of 0.875.
Among the numerical predictors, the series has 7 numerical features detected as Stationary out of the 7 numerical features using the dickey-fuller test and the rest are Unstationary. 7 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.066. to 3.096 and kurtosis values of 0.20 to 10.86. The fractal dimension analysis yields values ranging from -0.66 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.45, maximum 1.00, mean 0.29, and standard deviation 0.53. The count of numerical predictors with outliers is 7 with the minimum percentage of 0.11%, maximum percentage of 16.20%, average percentage of 2.61%, and standard deviation percentage of 6.00%.
Among the categorical predictors, the count of symbols ranges from 41 to 73 with a minimum entropy value 0.9917708855272739, maximum entropy 5.76740921961984, mean 4.546935167528349, and standard deviation 1.3789044013708147,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-27-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 7690 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7690 samples the target ground-truth class has changed 1053 times representing a percentage of 13.75%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.001. to 14.683 and kurtosis values of 0.74 to 283.12. The fractal dimension analysis yields values ranging from -0.61 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.96, maximum 1.00, mean 0.13, and standard deviation 0.53. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.60%, maximum percentage of 48.89%, average percentage of 38.41%, and standard deviation percentage of 13.70%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1020-11-3-classification.csv | A multivariate classification time-series dataset consists of 7012 samples and 11 features with 9 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.40 showing a Unbalanced dataset. Among the 7012 samples the target ground-truth class has changed 692 times representing a percentage of 9.92%. There are 11 features in the dataset with a ratio of numerical to categorical features of 4.5.
Among the numerical predictors, the series has 8 numerical features detected as Stationary out of the 9 numerical features using the dickey-fuller test and the rest are Unstationary. 9 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 41 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 1168,1168,2337 The numerical predictors also exhibit skewness values ranging from 0.320. to 7.001 and kurtosis values of 0.32 to 135.72. The fractal dimension analysis yields values ranging from -0.69 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.81, maximum 1.00, mean 0.14, and standard deviation 0.48. The count of numerical predictors with outliers is 7 with the minimum percentage of 0.00%, maximum percentage of 10.07%, average percentage of 3.82%, and standard deviation percentage of 3.48%.
Among the categorical predictors, the count of symbols ranges from 17 to 71 with a minimum entropy value 0.6340879817761534, maximum entropy 3.9763533477476853, mean 2.3052206647619196, and standard deviation 1.671132682985766,
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 5253.406843896113, 'penalty': 'l1', 'solver': 'saga'} |
1031-61-1-8-classification.csv | A multivariate classification time-series dataset consists of 7668 samples and 13 features with 13 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.48 showing a Unbalanced dataset. Among the 7668 samples the target ground-truth class has changed 1762 times representing a percentage of 23.08%. There are 13 features in the dataset
Among the numerical predictors, the series has 13 numerical features detected as Stationary out of the 13 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.115. to 12.575 and kurtosis values of 0.07 to 190.12. The fractal dimension analysis yields values ranging from -0.68 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.96, maximum 1.00, mean 0.16, and standard deviation 0.53. The count of numerical predictors with outliers is 13 with the minimum percentage of 0.24%, maximum percentage of 10.44%, average percentage of 5.10%, and standard deviation percentage of 3.50%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-13-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7019 samples and 16 features with 15 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.47 showing a Unbalanced dataset. Among the 7019 samples the target ground-truth class has changed 1446 times representing a percentage of 20.70%. There are 16 features in the dataset with a ratio of numerical to categorical features of 15.0.
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.032. to 1.871 and kurtosis values of 0.01 to 5.76. The fractal dimension analysis yields values ranging from -0.65 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.95, maximum 1.00, mean 0.11, and standard deviation 0.53. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.57%, maximum percentage of 12.78%, average percentage of 6.00%, and standard deviation percentage of 4.13%.
Among the categorical predictors, the count of symbols ranges from 96 to 96 with a minimum entropy value 0.5055340950921643, maximum entropy 0.5055340950921643, mean 0.5055340950921643, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-59-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7032 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 7032 samples the target ground-truth class has changed 1545 times representing a percentage of 22.08%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.072. to 16.371 and kurtosis values of 0.10 to 367.81. The fractal dimension analysis yields values ranging from -0.77 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.15, and standard deviation 0.50. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 14.58%, average percentage of 5.55%, and standard deviation percentage of 4.92%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1016-9-2-2-classification.csv | A multivariate classification time-series dataset consists of 7109 samples and 12 features with 5 numerical and 7 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 7109 samples the target ground-truth class has changed 294 times representing a percentage of 4.16%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.7142857142857143.
Among the numerical predictors, the series has 5 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 1 seasonality components detected in the numerical predictors. The top 1 common seasonality components are represented using sinusoidal waves. of periods 173 The numerical predictors also exhibit skewness values ranging from 0.248. to 0.943 and kurtosis values of 0.13 to 1.06. The fractal dimension analysis yields values ranging from -0.52 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.22, maximum 1.00, mean 0.39, and standard deviation 0.53. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 0.88%, average percentage of 0.20%, and standard deviation percentage of 0.38%.
Among the categorical predictors, the count of symbols ranges from 9 to 65 with a minimum entropy value 1.090161445904821, maximum entropy 4.984819413100032, mean 3.470237622733197, and standard deviation 1.4678824816132992,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.00055, 'penalty': 'elasticnet', 'solver': 'saga'} |
1016-19-3-1-classification.csv | A multivariate classification time-series dataset consists of 7210 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 23 to 23 with mean 23.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7210 samples the target ground-truth class has changed 398 times representing a percentage of 5.56%. There are 8 features in the dataset with a ratio of numerical to categorical features of 1.0.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 11 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 449,1197,1437 The numerical predictors also exhibit skewness values ranging from 0.032. to 0.678 and kurtosis values of 0.04 to 1.11. The fractal dimension analysis yields values ranging from -0.35 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.04, maximum 1.00, mean 0.61, and standard deviation 0.48. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.01%, maximum percentage of 2.24%, average percentage of 0.94%, and standard deviation percentage of 0.98%.
Among the categorical predictors, the count of symbols ranges from 40 to 62 with a minimum entropy value 1.451852228419936, maximum entropy 5.2532812953956, mean 3.934294176878676, and standard deviation 1.4791199621723992,
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 65645.63629085208, 'penalty': 'l1', 'solver': 'saga'} |
1031-13-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 5794 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.44 showing a Unbalanced dataset. Among the 5794 samples the target ground-truth class has changed 1119 times representing a percentage of 19.43%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.046. to 14.678 and kurtosis values of 0.04 to 278.05. The fractal dimension analysis yields values ranging from -0.65 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.90, maximum 1.00, mean 0.09, and standard deviation 0.50. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 14.83%, average percentage of 6.89%, and standard deviation percentage of 4.49%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1030-349-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.36 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 50 times representing a percentage of 1.21%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 1.692. to 2.720 and kurtosis values of 2.24 to 13.40. The fractal dimension analysis yields values ranging from -0.59 to -0.29 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.06, maximum 1.00, mean 0.66, and standard deviation 0.49. The count of numerical predictors with outliers is 5 with the minimum percentage of 4.85%, maximum percentage of 6.72%, average percentage of 6.33%, and standard deviation percentage of 0.83%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-21-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7056 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7056 samples the target ground-truth class has changed 1106 times representing a percentage of 15.75%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.022. to 19.561 and kurtosis values of 0.40 to 541.10. The fractal dimension analysis yields values ranging from -0.72 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.13, and standard deviation 0.47. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.45%, maximum percentage of 49.66%, average percentage of 37.09%, and standard deviation percentage of 14.14%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-29-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7464 samples and 13 features with 12 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 7464 samples the target ground-truth class has changed 1627 times representing a percentage of 21.90%. There are 13 features in the dataset with a ratio of numerical to categorical features of 12.0.
Among the numerical predictors, the series has 12 numerical features detected as Stationary out of the 12 numerical features using the dickey-fuller test and the rest are Unstationary. 10 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.012. to 1.040 and kurtosis values of 0.02 to 4.38. The fractal dimension analysis yields values ranging from -0.44 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.96, maximum 1.00, mean 0.20, and standard deviation 0.58. The count of numerical predictors with outliers is 12 with the minimum percentage of 0.38%, maximum percentage of 13.76%, average percentage of 6.88%, and standard deviation percentage of 5.07%.
Among the categorical predictors, the count of symbols ranges from 129 to 129 with a minimum entropy value 0.49910175173727767, maximum entropy 0.49910175173727767, mean 0.49910175173727767, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1031-21-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 6752 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.45 showing a Unbalanced dataset. Among the 6752 samples the target ground-truth class has changed 970 times representing a percentage of 14.44%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.108. to 15.669 and kurtosis values of 0.04 to 308.98. The fractal dimension analysis yields values ranging from -0.72 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.13, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.28%, maximum percentage of 16.25%, average percentage of 8.34%, and standard deviation percentage of 3.99%.
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 0.5, 'penalty': 'l1', 'solver': 'saga'} |
1016-17-6-1-classification.csv | A multivariate classification time-series dataset consists of 7110 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.98 showing a Balanced dataset. Among the 7110 samples the target ground-truth class has changed 280 times representing a percentage of 3.95%. There are 8 features in the dataset with a ratio of numerical to categorical features of 1.0.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.135. to 0.451 and kurtosis values of 0.55 to 0.65. The fractal dimension analysis yields values ranging from -0.42 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.13, maximum 1.00, mean 0.67, and standard deviation 0.41. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.03%, maximum percentage of 1.89%, average percentage of 0.52%, and standard deviation percentage of 0.91%.
Among the categorical predictors, the count of symbols ranges from 41 to 57 with a minimum entropy value 1.7445183444243388, maximum entropy 5.036343058106777, mean 4.139842858513349, and standard deviation 1.3842447963476143,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-50-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 7044 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.44 showing a Unbalanced dataset. Among the 7044 samples the target ground-truth class has changed 1426 times representing a percentage of 20.34%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.021. to 14.942 and kurtosis values of 0.07 to 307.57. The fractal dimension analysis yields values ranging from -0.68 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.83, maximum 1.00, mean 0.09, and standard deviation 0.50. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 12.98%, average percentage of 6.57%, and standard deviation percentage of 3.87%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 10, 'reg_lambda': 0.2} |
3001-71.csv | A multivariate classification time-series dataset consists of 648 samples and 2 features with 2 numerical and 0 categorical features. Each instance has a window length of 3. The dataset has a sampling rate of 480.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 4 classes with entropy value 1.39 showing a Unbalanced dataset. Among the 648 samples the target ground-truth class has changed 438 times representing a percentage of 68.87%. There are 2 features in the dataset
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 2 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.040. to 1.121 and kurtosis values of 0.03 to 0.57. The fractal dimension analysis yields values ranging from -1.30 to -1.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.49, maximum 1.00, mean 0.26, and standard deviation 0.74. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 0.16%, average percentage of 0.08%, and standard deviation percentage of 0.11%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 5, 'n_estimators': 100} |
1031-21-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 6860 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.51 showing a Unbalanced dataset. Among the 6860 samples the target ground-truth class has changed 1402 times representing a percentage of 20.54%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.001. to 14.996 and kurtosis values of 0.08 to 290.17. The fractal dimension analysis yields values ranging from -0.67 to -0.14 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.95, maximum 1.00, mean 0.12, and standard deviation 0.46. The count of numerical predictors with outliers is 14 with the minimum percentage of 0.00%, maximum percentage of 18.28%, average percentage of 4.23%, and standard deviation percentage of 4.57%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1016-11-4-4-classification.csv | A multivariate classification time-series dataset consists of 7109 samples and 12 features with 4 numerical and 8 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7109 samples the target ground-truth class has changed 279 times representing a percentage of 3.94%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.5.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 3 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 124,139,245 The numerical predictors also exhibit skewness values ranging from 0.040. to 0.436 and kurtosis values of 0.14 to 0.78. The fractal dimension analysis yields values ranging from -0.41 to -0.10 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.01, maximum 1.00, mean 0.62, and standard deviation 0.47. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 0.66%, average percentage of 0.17%, and standard deviation percentage of 0.33%.
Among the categorical predictors, the count of symbols ranges from 9 to 71 with a minimum entropy value 1.4749501967344156, maximum entropy 5.076116462395573, mean 3.6508756110576144, and standard deviation 1.419884054199513,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-9-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 7900 samples and 15 features with 15 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.48 showing a Unbalanced dataset. Among the 7900 samples the target ground-truth class has changed 1592 times representing a percentage of 20.24%. There are 15 features in the dataset
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 12 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.033. to 3.092 and kurtosis values of 0.19 to 11.17. The fractal dimension analysis yields values ranging from -0.63 to -0.10 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.95, maximum 1.00, mean 0.16, and standard deviation 0.53. The count of numerical predictors with outliers is 14 with the minimum percentage of 0.00%, maximum percentage of 14.00%, average percentage of 4.21%, and standard deviation percentage of 4.52%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1031-9-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 7568 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 7568 samples the target ground-truth class has changed 380 times representing a percentage of 5.04%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.003. to 3.597 and kurtosis values of 4.51 to 34.63. The fractal dimension analysis yields values ranging from -0.52 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.08, and standard deviation 0.46. The count of numerical predictors with outliers is 16 with the minimum percentage of 14.76%, maximum percentage of 14.76%, average percentage of 14.76%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1016-13-5-5-classification.csv | A multivariate classification time-series dataset consists of 6594 samples and 8 features with 5 numerical and 3 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 25 to 25 with mean 25.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 6594 samples the target ground-truth class has changed 290 times representing a percentage of 4.44%. There are 8 features in the dataset with a ratio of numerical to categorical features of 1.6666666666666667.
Among the numerical predictors, the series has 5 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 9 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 182,218,285 The numerical predictors also exhibit skewness values ranging from 0.053. to 0.665 and kurtosis values of 0.08 to 0.72. The fractal dimension analysis yields values ranging from -0.39 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.19, maximum 1.00, mean 0.49, and standard deviation 0.50. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 1.45%, average percentage of 0.50%, and standard deviation percentage of 0.65%.
Among the categorical predictors, the count of symbols ranges from 45 to 59 with a minimum entropy value 1.1726974081783978, maximum entropy 4.959398203862342, mean 3.604955846132589, and standard deviation 1.723570200891381,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 125.0, 'l1_ratio': 0.0003, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-33-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7472 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 5594 with mean 349.62 and standard deviation 1398.50.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 7472 samples the target ground-truth class has changed 375 times representing a percentage of 5.04%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.035. to 26.041 and kurtosis values of 4.12 to 884.01. The fractal dimension analysis yields values ranging from -0.64 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.95, maximum 1.00, mean 0.11, and standard deviation 0.57. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.64%, maximum percentage of 22.25%, average percentage of 21.02%, and standard deviation percentage of 4.90%.
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 223.60679774997894, 'penalty': 'l1', 'solver': 'saga'} |
1031-24-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7605 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7605 samples the target ground-truth class has changed 377 times representing a percentage of 4.98%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.972. to 13.358 and kurtosis values of 1.84 to 179.15. The fractal dimension analysis yields values ranging from -0.39 to -0.05 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.13, maximum 1.00, mean 0.81, and standard deviation 0.34. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.53%, maximum percentage of 27.29%, average percentage of 4.79%, and standard deviation percentage of 9.50%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 0.1, 'n_estimators': 50} |
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