dataset_name stringlengths 8 32 | series_description stringlengths 1.32k 2.25k | algorithm stringclasses 8 values | hyperparameters stringclasses 93 values |
|---|---|---|---|
1031-39-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7593 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 7593 samples the target ground-truth class has changed 718 times representing a percentage of 9.50%. 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. 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 0.059. to 1.106 and kurtosis values of 1.21 to 6.82. 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 -0.75, maximum 1.00, mean 0.10, and standard deviation 0.57. The count of numerical predictors with outliers is 15 with the minimum percentage of 36.49%, maximum percentage of 36.49%, average percentage of 36.49%, and standard deviation percentage of 0.00%.
Among the categorical predictors, the count of symbols ranges from 38 to 38 with a minimum entropy value 0.13175662637799326, maximum entropy 0.13175662637799326, mean 0.13175662637799326, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 181.8181818181818, 'l1_ratio': 0.001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-41-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7530 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 7530 samples the target ground-truth class has changed 1352 times representing a percentage of 18.04%. 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. 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 0.074. to 14.291 and kurtosis values of 0.01 to 259.65. 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 -0.97, maximum 1.00, mean 0.11, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.01%, maximum percentage of 27.45%, average percentage of 13.90%, and standard deviation percentage of 7.12%.
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} |
1030-358-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 33 times representing a percentage of 0.80%. 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 0.443. to 3.516 and kurtosis values of 0.11 to 24.73. The fractal dimension analysis yields values ranging from -0.59 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 -0.47, maximum 1.00, mean 0.53, and standard deviation 0.68. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.68%, maximum percentage of 4.68%, average percentage of 1.51%, and standard deviation percentage of 1.77%.
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'} |
1030-23-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 21 times representing a percentage of 0.51%. 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 1.239. to 3.925 and kurtosis values of 0.56 to 30.54. The fractal dimension analysis yields values ranging from -0.53 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 -0.04, maximum 1.00, mean 0.67, and standard deviation 0.48. 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'} |
1031-8-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 7914 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 5493 with mean 343.31 and standard deviation 1373.25.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 7914 samples the target ground-truth class has changed 513 times representing a percentage of 6.51%. 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 0.087. to 23.459 and kurtosis values of 3.75 to 660.68. 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 -0.86, maximum 1.00, mean 0.14, and standard deviation 0.52. The count of numerical predictors with outliers is 16 with the minimum percentage of 25.20%, maximum percentage of 26.79%, average percentage of 26.69%, and standard deviation percentage of 0.40%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 181.8181818181818, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1030-322-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 24 times representing a percentage of 0.58%. 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 0.251. to 6.957 and kurtosis values of 0.91 to 91.74. The fractal dimension analysis yields values ranging from -0.58 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 -0.08, maximum 1.00, mean 0.66, and standard deviation 0.50. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 4.99%, average percentage of 1.00%, and standard deviation percentage of 2.23%.
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} |
1031-42-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 6288 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 6288 samples the target ground-truth class has changed 1518 times representing a percentage of 24.27%. 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 0.135. to 8.178 and kurtosis values of 0.02 to 91.52. The fractal dimension analysis yields values ranging from -0.69 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 -0.67, maximum 1.00, mean 0.22, and standard deviation 0.47. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.49%, maximum percentage of 11.94%, average percentage of 6.97%, and standard deviation percentage of 3.36%.
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} |
1016-17-6-2-classification.csv | A multivariate classification time-series dataset consists of 7109 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 7109 samples the target ground-truth class has changed 229 times representing a percentage of 3.24%. 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 0.162. to 0.589 and kurtosis values of 0.40 to 0.62. The fractal dimension analysis yields values ranging from -0.44 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 -0.08, maximum 1.00, mean 0.59, and standard deviation 0.51. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 1.26%, average percentage of 0.33%, and standard deviation percentage of 0.62%.
Among the categorical predictors, the count of symbols ranges from 38 to 54 with a minimum entropy value 1.4329720078071184, maximum entropy 5.0771437368076695, mean 4.057841920267341, and standard deviation 1.519436797648278,
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 0.7616652503521798, 'penalty': 'l1', 'solver': 'saga'} |
1031-20-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7609 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 7609 samples the target ground-truth class has changed 1463 times representing a percentage of 19.31%. 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 0.023. to 11.328 and kurtosis values of 0.02 to 162.00. 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 -0.97, maximum 1.00, mean 0.11, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.71%, maximum percentage of 13.20%, average percentage of 6.34%, and standard deviation percentage of 4.16%.
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} |
1031-19-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 7708 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 7708 samples the target ground-truth class has changed 840 times representing a percentage of 10.95%. 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 0.057. to 2.282 and kurtosis values of 1.23 to 14.37. The fractal dimension analysis yields values ranging from -0.48 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 -0.93, 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 40.68%, maximum percentage of 40.68%, average percentage of 40.68%, and standard deviation percentage of 0.00%.
Among the categorical predictors, the count of symbols ranges from 123 to 123 with a minimum entropy value 0.3225488005910243, maximum entropy 0.3225488005910243, mean 0.3225488005910243, 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} |
1031-19-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7705 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 1.48 showing a Unbalanced dataset. Among the 7705 samples the target ground-truth class has changed 1500 times representing a percentage of 19.55%. 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 0.027. to 21.240 and kurtosis values of 0.06 to 603.40. The fractal dimension analysis yields values ranging from -0.64 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 -0.52, maximum 1.00, mean 0.19, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.14%, maximum percentage of 13.27%, average percentage of 4.28%, and standard deviation percentage of 3.58%.
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} |
1016-4-3-1-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 290 times representing a percentage of 4.10%. 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 1 seasonality components detected in the numerical predictors. The top 1 common seasonality components are represented using sinusoidal waves. of periods 444 The numerical predictors also exhibit skewness values ranging from 0.033. to 0.436 and kurtosis values of 0.03 to 0.59. The fractal dimension analysis yields values ranging from -0.53 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 0.24, maximum 1.00, mean 0.71, and standard deviation 0.36. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 0.71%, average percentage of 0.22%, and standard deviation percentage of 0.33%.
Among the categorical predictors, the count of symbols ranges from 9 to 60 with a minimum entropy value 1.6452230202501679, maximum entropy 5.1131552503189415, mean 3.710448141221307, and standard deviation 1.3777984563177206,
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} |
1020-38-2-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 1.45 showing a Unbalanced dataset. Among the 7010 samples the target ground-truth class has changed 946 times representing a percentage of 13.56%. 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 39 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 467,500,637 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 0.47. The count of numerical predictors with outliers is 9 with the minimum percentage of 0.01%, maximum percentage of 9.52%, average percentage of 3.70%, 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': 50} |
1031-12-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 1456 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 1456 samples the target ground-truth class has changed 304 times representing a percentage of 21.38%. 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. 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 0.012. to 11.283 and kurtosis values of 0.13 to 150.65. The fractal dimension analysis yields values ranging from -0.69 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 -0.98, 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 12.31%, average percentage of 7.64%, and standard deviation percentage of 3.29%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 40, 'n_estimators': 100} |
3001-59.csv | A multivariate classification time-series dataset consists of 1168 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 0 to 0 with mean 0.00
The target column has 5 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 1168 samples the target ground-truth class has changed 222 times representing a percentage of 19.20%. 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 0.885. to 0.885 and kurtosis values of 0.31 to 0.31. The fractal dimension analysis yields values ranging from -0.64 to -0.64 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 0.87%, maximum percentage of 0.87%, average percentage of 0.87%,
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} |
1031-48-2-1-6-classification.csv | A multivariate classification time-series dataset consists of 7457 samples and 16 features with 14 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 1725 with mean 123.21 and standard deviation 461.03. Similarly, the missing values percentages for categorical features range from 1725 to 1726 with mean 1725.5 and standard deviation 0.7071067811865476.
The target column has 3 classes with entropy value 1.55 showing a Unbalanced dataset. Among the 7457 samples the target ground-truth class has changed 965 times representing a percentage of 16.94%. There are 16 features in the dataset with a ratio of numerical to categorical features of 7.0.
Among the numerical predictors, the series has 14 numerical features detected as Stationary out of the 14 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 0.169. to 2.318 and kurtosis values of 0.01 to 9.77. The fractal dimension analysis yields values ranging from -0.80 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 -0.92, maximum 1.00, mean 0.11, and standard deviation 0.53. The count of numerical predictors with outliers is 12 with the minimum percentage of 0.00%, maximum percentage of 5.51%, average percentage of 1.62%, and standard deviation percentage of 1.96%.
Among the categorical predictors, the count of symbols ranges from 34 to 59 with a minimum entropy value 1.0428681466798169, maximum entropy 4.448853397388219, mean 2.745860772034018, and standard deviation 1.7029926253542012,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1030-237-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 1.00 showing a Balanced dataset. Among the 4140 samples the target ground-truth class has changed 19 times representing a percentage of 0.46%. 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 0.008. to 2.267 and kurtosis values of 1.24 to 10.03. 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 0.21, maximum 1.00, mean 0.75, and standard deviation 0.36. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 4.51%, average percentage of 0.90%, and standard deviation percentage of 2.02%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 20, 'n_estimators': 100} |
1020-35-4-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 1.42 showing a Unbalanced dataset. Among the 7012 samples the target ground-truth class has changed 795 times representing a percentage of 11.39%. 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 44 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 1001,1753,3506 The numerical predictors also exhibit skewness values ranging from 0.386. to 3.546 and kurtosis values of 0.38 to 30.97. 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 -0.79, maximum 1.00, mean 0.13, and standard deviation 0.49. The count of numerical predictors with outliers is 7 with the minimum percentage of 0.00%, maximum percentage of 10.99%, average percentage of 3.10%, and standard deviation percentage of 3.57%.
Among the categorical predictors, the count of symbols ranges from 17 to 74 with a minimum entropy value 0.47914086595555105, maximum entropy 3.6986447338513884, mean 2.0888927999034697, and standard deviation 1.6097519339479187,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1030-288-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 1.42 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 36 times representing a percentage of 0.87%. 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 0.930. to 11.877 and kurtosis values of 0.14 to 264.85. The fractal dimension analysis yields values ranging from -0.66 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 -0.03, maximum 1.00, mean 0.67, and standard deviation 0.47. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.02%, maximum percentage of 5.72%, average percentage of 3.78%, and standard deviation percentage of 2.18%.
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'} |
3001-26.csv | A multivariate classification time-series dataset consists of 384 samples and 5 features with 5 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 1 with mean 0.60 and standard deviation 0.55.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 384 samples the target ground-truth class has changed 352 times representing a percentage of 95.14%. There are 5 features in the dataset
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 5 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 1 seasonality components detected in the numerical predictors. The top 1 common seasonality components are represented using sinusoidal waves. of periods 27 The numerical predictors also exhibit skewness values ranging from 0.081. to 1.422 and kurtosis values of 0.00 to 1.83. The fractal dimension analysis yields values ranging from -1.80 to -1.18 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.67, maximum 1.00, mean 0.15, and standard deviation 0.63. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 5.41%, average percentage of 1.24%, and standard deviation percentage of 2.34%.
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'} |
1031-50-2-1-3-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 1.36 showing a Unbalanced dataset. Among the 7044 samples the target ground-truth class has changed 1079 times representing a percentage of 15.39%. 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 0.137. to 19.971 and kurtosis values of 0.43 to 486.33. 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 -0.83, maximum 1.00, mean 0.08, and standard deviation 0.51. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.17%, maximum percentage of 48.63%, average percentage of 36.32%, and standard deviation percentage of 13.77%.
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'} |
3001-62.csv | A multivariate classification time-series dataset consists of 288 samples and 4 features with 4 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 1 with mean 0.25 and standard deviation 0.50.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 288 samples the target ground-truth class has changed 273 times representing a percentage of 99.64%. There are 4 features in the dataset
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. 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 0.020. to 1.431 and kurtosis values of 0.10 to 2.05. The fractal dimension analysis yields values ranging from -1.25 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 -0.71, maximum 1.00, mean 0.29, and standard deviation 0.68. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 4.38%, average percentage of 1.09%, and standard deviation percentage of 2.19%.
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': 100} |
1031-52-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 4819 samples and 10 features with 10 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 4819 samples the target ground-truth class has changed 464 times representing a percentage of 9.70%. There are 10 features in the dataset
Among the numerical predictors, the series has 10 numerical features detected as Stationary out of the 10 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 0.008. to 31.157 and kurtosis values of 1.67 to 1309.09. The fractal dimension analysis yields values ranging from -0.73 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 -0.41, maximum 1.00, mean 0.24, and standard deviation 0.43. The count of numerical predictors with outliers is 10 with the minimum percentage of 2.15%, maximum percentage of 38.68%, average percentage of 35.03%, and standard deviation percentage of 11.55%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-47-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7398 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 7398 samples the target ground-truth class has changed 1458 times representing a percentage of 19.80%. 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 0.045. to 11.673 and kurtosis values of 0.05 to 173.15. 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 -0.80, maximum 1.00, mean 0.09, and standard deviation 0.52. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.88%, maximum percentage of 15.18%, average percentage of 6.46%, and standard deviation percentage of 5.12%.
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} |
1031-11-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 6671 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 6671 samples the target ground-truth class has changed 660 times representing a percentage of 9.94%. 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 0.195. to 3.625 and kurtosis values of 1.72 to 11.27. 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 0.30, maximum 1.00, mean 0.91, and standard deviation 0.23. The count of numerical predictors with outliers is 14 with the minimum percentage of 0.00%, maximum percentage of 22.18%, average percentage of 17.14%, and standard deviation percentage of 4.99%.
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} |
1031-8-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 7915 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 1.47 showing a Unbalanced dataset. Among the 7915 samples the target ground-truth class has changed 1653 times representing a percentage of 20.97%. 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 0.003. to 24.488 and kurtosis values of 0.06 to 768.84. 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 -0.97, maximum 1.00, mean 0.11, and standard deviation 0.51. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.05%, maximum percentage of 11.64%, average percentage of 5.89%, and standard deviation percentage of 4.60%.
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} |
1031-23-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 6188 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 6188 samples the target ground-truth class has changed 1270 times representing a percentage of 20.64%. 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 0.018. to 1.417 and kurtosis values of 0.04 to 2.77. The fractal dimension analysis yields values ranging from -0.58 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 -0.94, maximum 1.00, mean 0.08, and standard deviation 0.56. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.13%, maximum percentage of 10.69%, average percentage of 5.95%, and standard deviation percentage of 3.85%.
Among the categorical predictors, the count of symbols ranges from 104 to 104 with a minimum entropy value 0.457709398689407, maximum entropy 0.457709398689407, mean 0.457709398689407, 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} |
1016-9-5-3-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 296 times representing a percentage of 4.18%. 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. 3 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 2 seasonality components detected in the numerical predictors. The top 2 common seasonality components are represented using sinusoidal waves. of periods 74,142 The numerical predictors also exhibit skewness values ranging from 0.076. to 1.178 and kurtosis values of 0.17 to 0.76. The fractal dimension analysis yields values ranging from -0.49 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 -0.30, maximum 1.00, mean 0.40, and standard deviation 0.54. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 23.42%, average percentage of 4.98%, and standard deviation percentage of 10.33%.
Among the categorical predictors, the count of symbols ranges from 9 to 59 with a minimum entropy value 1.331255935366179, maximum entropy 5.237508135786311, mean 3.434444259090282, and standard deviation 1.465064481584549,
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} |
1016-4-2-3-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 338 times representing a percentage of 4.78%. 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 4 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 78,88,1421 The numerical predictors also exhibit skewness values ranging from 0.188. to 0.785 and kurtosis values of 0.25 to 0.37. The fractal dimension analysis yields values ranging from -0.49 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 0.10, 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 0.18%, maximum percentage of 1.63%, average percentage of 0.60%, and standard deviation percentage of 0.69%.
Among the categorical predictors, the count of symbols ranges from 9 to 59 with a minimum entropy value 1.4697018357667242, maximum entropy 5.094435512812909, mean 3.6858754421896958, and standard deviation 1.4473057185838694,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1031-57-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 6270 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 6270 samples the target ground-truth class has changed 893 times representing a percentage of 14.32%. 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 0.011. to 3.742 and kurtosis values of 0.89 to 20.02. The fractal dimension analysis yields values ranging from -0.57 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 -0.94, maximum 1.00, mean 0.15, and standard deviation 0.57. The count of numerical predictors with outliers is 15 with the minimum percentage of 12.78%, maximum percentage of 48.67%, average percentage of 43.51%, and standard deviation percentage of 8.93%.
Among the categorical predictors, the count of symbols ranges from 58 to 58 with a minimum entropy value 0.3337223470588407, maximum entropy 0.3337223470588407, mean 0.3337223470588407, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 40, 'n_estimators': 200} |
3001-38.csv | A multivariate classification time-series dataset consists of 1056 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 0 to 0 with mean 0.00
The target column has 4 classes with entropy value 1.73 showing a Unbalanced dataset. Among the 1056 samples the target ground-truth class has changed 1041 times representing a percentage of 99.90%. 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 1.200. to 1.200 and kurtosis values of 0.73 to 0.73. The fractal dimension analysis yields values ranging from -1.54 to -1.54 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 0.96%, maximum percentage of 0.96%, average percentage of 0.96%,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 10, 'reg_lambda': 0.2} |
1031-48-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 7451 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 4634 with mean 289.62 and standard deviation 1158.50.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 7451 samples the target ground-truth class has changed 288 times representing a percentage of 3.88%. 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 0.013. to 21.880 and kurtosis values of 1.55 to 692.53. The fractal dimension analysis yields values ranging from -0.74 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 -0.97, maximum 1.00, mean 0.10, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.52%, maximum percentage of 37.09%, average percentage of 34.99%, and standard deviation percentage of 8.39%.
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} |
1016-16-1-1-classification.csv | A multivariate classification time-series dataset consists of 3541 samples and 5 features with 5 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 1.38 showing a Unbalanced dataset. Among the 3541 samples the target ground-truth class has changed 266 times representing a percentage of 7.58%. There are 5 features in the dataset
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. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 18 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 590,708,1180 The numerical predictors also exhibit skewness values ranging from 0.212. to 0.941 and kurtosis values of 1.54 to 3.87. The fractal dimension analysis yields values ranging from -0.54 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 -0.59, maximum 1.00, mean 0.28, and standard deviation 0.48. The count of numerical predictors with outliers is 5 with the minimum percentage of 23.18%, maximum percentage of 30.31%, average percentage of 26.59%, and standard deviation percentage of 2.63%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 100.0, 'l1_ratio': 0.0007999999999999999, 'penalty': 'elasticnet', 'solver': 'saga'} |
1016-18-2-6-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 17 to 17 with mean 17.0 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 251 times representing a percentage of 3.51%. 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 899 The numerical predictors also exhibit skewness values ranging from 0.037. to 0.408 and kurtosis values of 0.17 to 0.88. The fractal dimension analysis yields values ranging from -0.38 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 0.09, maximum 1.00, mean 0.65, and standard deviation 0.43. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 1.33%, average percentage of 0.34%, and standard deviation percentage of 0.66%.
Among the categorical predictors, the count of symbols ranges from 46 to 71 with a minimum entropy value 1.6202029217517422, maximum entropy 5.1504143017022175, mean 3.9455756413181278, and standard deviation 1.3838327892045592,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-10-3-1-3-classification.csv | A multivariate classification time-series dataset consists of 6668 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 6668 samples the target ground-truth class has changed 1342 times representing a percentage of 20.23%. 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 0.015. to 20.552 and kurtosis values of 0.12 to 565.15. 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 -0.89, maximum 1.00, mean 0.11, and standard deviation 0.49. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 13.79%, average percentage of 6.73%, and standard deviation percentage of 4.61%.
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} |
1031-58-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 6747 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 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 6747 samples the target ground-truth class has changed 400 times representing a percentage of 5.96%. 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 6.252. to 14.181 and kurtosis values of 50.96 to 203.39. 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 0.60, maximum 1.00, mean 0.90, and standard deviation 0.17. The count of numerical predictors with outliers is 13 with the minimum percentage of 18.92%, maximum percentage of 18.92%, average percentage of 18.92%, 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 0.04247505385150359, maximum entropy 0.04247505385150359, mean 0.04247505385150359, 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=1), 'learning_rate': 0.1, 'n_estimators': 50} |
3001-5.csv | A multivariate classification time-series dataset consists of 216 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 216 samples the target ground-truth class has changed 135 times representing a percentage of 66.50%. 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 0.073. to 0.073 and kurtosis values of 1.36 to 1.36. The fractal dimension analysis yields values ranging from -1.52 to -1.52 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 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': 0.1, 'n_estimators': 50} |
1031-41-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 7288 samples and 8 features with 8 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 7288 samples the target ground-truth class has changed 1190 times representing a percentage of 16.40%. There are 8 features in the dataset
Among the numerical predictors, the series has 8 numerical features detected as Stationary out of the 8 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 0.037. to 12.010 and kurtosis values of 0.75 to 178.90. The fractal dimension analysis yields values ranging from -0.60 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 -0.35, maximum 1.00, mean 0.24, and standard deviation 0.49. The count of numerical predictors with outliers is 8 with the minimum percentage of 2.91%, maximum percentage of 37.62%, average percentage of 29.37%, and standard deviation percentage of 13.42%.
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} |
1016-21-1-5-classification.csv | A multivariate classification time-series dataset consists of 7212 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 1.00 showing a Balanced dataset. Among the 7212 samples the target ground-truth class has changed 385 times representing a percentage of 5.36%. 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 5 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 379,515,600 The numerical predictors also exhibit skewness values ranging from 0.083. to 0.447 and kurtosis values of 0.02 to 0.62. The fractal dimension analysis yields values ranging from -0.38 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 0.01, maximum 1.00, mean 0.62, and standard deviation 0.46. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.01%, maximum percentage of 1.03%, average percentage of 0.32%, and standard deviation percentage of 0.48%.
Among the categorical predictors, the count of symbols ranges from 25 to 69 with a minimum entropy value 1.441017132509863, maximum entropy 5.2316167961477555, mean 3.905855857335237, and standard deviation 1.5297390887044484,
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} |
1031-41-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 7537 samples and 14 features with 10 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 0.97 showing a Unbalanced dataset. Among the 7537 samples the target ground-truth class has changed 706 times representing a percentage of 9.41%. There are 14 features in the dataset with a ratio of numerical to categorical features of 2.5.
Among the numerical predictors, the series has 10 numerical features detected as Stationary out of the 10 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 0.034. to 1.294 and kurtosis values of 0.11 to 3.03. The fractal dimension analysis yields values ranging from -0.74 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 -0.34, maximum 1.00, mean 0.22, and standard deviation 0.46. The count of numerical predictors with outliers is 9 with the minimum percentage of 0.00%, maximum percentage of 4.22%, average percentage of 1.41%, and standard deviation percentage of 1.51%.
Among the categorical predictors, the count of symbols ranges from 58 to 77 with a minimum entropy value 0.699001071894081, maximum entropy 5.474129561803013, mean 4.115076361490376, and standard deviation 1.9788230904419364,
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} |
1016-19-2-2-classification.csv | A multivariate classification time-series dataset consists of 7210 samples and 8 features with 6 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 218 with mean 36.33 and standard deviation 89.00. Similarly, the missing values percentages for categorical features range from 218 to 218 with mean 218.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.52 showing a Unbalanced dataset. Among the 7210 samples the target ground-truth class has changed 631 times representing a percentage of 9.07%. There are 8 features in the dataset with a ratio of numerical to categorical features of 3.0.
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 17 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 332,411,437 The numerical predictors also exhibit skewness values ranging from 0.042. to 0.833 and kurtosis values of 0.05 to 0.59. 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 -0.17, maximum 1.00, mean 0.32, and standard deviation 0.49. The count of numerical predictors with outliers is 6 with the minimum percentage of 0.03%, maximum percentage of 5.46%, average percentage of 1.91%, and standard deviation percentage of 2.02%.
Among the categorical predictors, the count of symbols ranges from 66 to 68 with a minimum entropy value 1.0782679943298261, maximum entropy 4.14248558257123, mean 2.610376788450528, and standard deviation 1.5321087941207021,
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'} |
1031-25-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7604 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 7604 samples the target ground-truth class has changed 383 times representing a percentage of 5.06%. 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 1.012. to 25.965 and kurtosis values of 3.32 to 676.14. The fractal dimension analysis yields values ranging from -0.47 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 0.02, maximum 1.00, mean 0.86, and standard deviation 0.29. The count of numerical predictors with outliers is 16 with the minimum percentage of 20.82%, maximum percentage of 20.82%, average percentage of 20.82%, 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=1), 'learning_rate': 0.1, 'n_estimators': 50} |
1030-384-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 0.98 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 37 times representing a percentage of 0.90%. 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 0.029. to 3.165 and kurtosis values of 1.04 to 23.05. The fractal dimension analysis yields values ranging from -0.63 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 0.50, and standard deviation 0.70. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.75%, average percentage of 1.15%, and standard deviation percentage of 2.57%.
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} |
1016-19-2-4-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 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 7210 samples the target ground-truth class has changed 258 times representing a percentage of 3.60%. 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. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 2 seasonality components detected in the numerical predictors. The top 2 common seasonality components are represented using sinusoidal waves. of periods 24,248 The numerical predictors also exhibit skewness values ranging from 0.193. to 0.646 and kurtosis values of 0.57 to 0.87. The fractal dimension analysis yields values ranging from -0.38 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 0.09, maximum 1.00, mean 0.66, and standard deviation 0.43. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 1.55%, average percentage of 0.39%, and standard deviation percentage of 0.77%.
Among the categorical predictors, the count of symbols ranges from 31 to 55 with a minimum entropy value 1.0878533403646282, maximum entropy 5.3489015274111, mean 3.8505258823021706, and standard deviation 1.656094201775381,
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} |
1031-34-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7678 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 7678 samples the target ground-truth class has changed 1596 times representing a percentage of 20.88%. 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 0.010. to 13.494 and kurtosis values of 0.22 to 226.23. The fractal dimension analysis yields values ranging from -0.63 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 0.11, and standard deviation 0.51. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.12%, maximum percentage of 14.25%, average percentage of 5.81%, and standard deviation percentage of 5.09%.
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} |
1031-89-1-4-classification.csv | A multivariate classification time-series dataset consists of 6996 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 1.48 showing a Unbalanced dataset. Among the 6996 samples the target ground-truth class has changed 1516 times representing a percentage of 21.78%. 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 13 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 0.107. to 1.165 and kurtosis values of 0.13 to 2.98. The fractal dimension analysis yields values ranging from -0.60 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 -0.88, maximum 1.00, mean 0.11, and standard deviation 0.53. The count of numerical predictors with outliers is 14 with the minimum percentage of 0.00%, maximum percentage of 12.61%, average percentage of 6.19%, and standard deviation percentage of 4.28%.
Among the categorical predictors, the count of symbols ranges from 90 to 90 with a minimum entropy value 0.4229408739200567, maximum entropy 0.4229408739200567, mean 0.4229408739200567, 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} |
1031-52-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 2214 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 1.43 showing a Unbalanced dataset. Among the 2214 samples the target ground-truth class has changed 388 times representing a percentage of 17.80%. 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 0.074. to 1.240 and kurtosis values of 0.01 to 2.65. The fractal dimension analysis yields values ranging from -0.68 to -0.06 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 0.20, and standard deviation 0.49. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.09%, maximum percentage of 13.12%, average percentage of 6.47%, and standard deviation percentage of 4.31%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 20, 'n_estimators': 400} |
1031-9-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7890 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 7890 samples the target ground-truth class has changed 556 times representing a percentage of 7.08%. 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 0.288. to 1.178 and kurtosis values of 0.23 to 1.74. The fractal dimension analysis yields values ranging from -0.27 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 0.45, maximum 1.00, mean 0.92, and standard deviation 0.13. The count of numerical predictors with outliers is 16 with the minimum percentage of 32.23%, maximum percentage of 32.23%, average percentage of 32.23%, 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} |
1031-55-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7088 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 7088 samples the target ground-truth class has changed 1146 times representing a percentage of 16.25%. 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 0.005. to 1.318 and kurtosis values of 0.18 to 2.84. The fractal dimension analysis yields values ranging from -0.59 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 -0.92, maximum 1.00, mean 0.15, and standard deviation 0.53. The count of numerical predictors with outliers is 15 with the minimum percentage of 2.62%, maximum percentage of 19.36%, average percentage of 14.57%, and standard deviation percentage of 3.77%.
Among the categorical predictors, the count of symbols ranges from 83 to 83 with a minimum entropy value 0.3119084619853738, maximum entropy 0.3119084619853738, mean 0.3119084619853738, 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} |
1031-41-2-1-6-classification.csv | A multivariate classification time-series dataset consists of 7508 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 0 to 0 with mean 0.0
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7508 samples the target ground-truth class has changed 676 times representing a percentage of 9.04%. 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 0.110. to 1.531 and kurtosis values of 0.10 to 6.66. The fractal dimension analysis yields values ranging from -0.76 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 0.53. The count of numerical predictors with outliers is 10 with the minimum percentage of 0.00%, maximum percentage of 4.44%, average percentage of 1.10%, and standard deviation percentage of 1.52%.
Among the categorical predictors, the count of symbols ranges from 87 to 87 with a minimum entropy value 0.5769495107525886, maximum entropy 0.5769495107525886, mean 0.5769495107525886, 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} |
1030-250-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 3 times representing a percentage of 0.07%. 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 0.006. to 10.198 and kurtosis values of 1.18 to 182.82. The fractal dimension analysis yields values ranging from -0.61 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 -0.02, maximum 1.00, mean 0.68, and standard deviation 0.47. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.97%, average percentage of 1.19%, and standard deviation percentage of 2.67%.
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} |
1031-25-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7046 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 7046 samples the target ground-truth class has changed 586 times representing a percentage of 8.36%. 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 0.060. to 20.693 and kurtosis values of 2.93 to 532.07. 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 -0.94, maximum 1.00, mean 0.12, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.78%, maximum percentage of 35.81%, average percentage of 33.68%, and standard deviation percentage of 8.51%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1030-207-classification.csv | A multivariate classification time-series dataset consists of 1456 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 1.00 showing a Balanced dataset. Among the 1456 samples the target ground-truth class has changed 33 times representing a percentage of 2.29%. There are 5 features in the dataset
Among the numerical predictors, the series has 2 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 0.276. to 4.805 and kurtosis values of 0.54 to 53.26. The fractal dimension analysis yields values ranging from -0.63 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 -0.13, maximum 1.00, mean 0.64, and standard deviation 0.51. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 5.90%, average percentage of 1.24%, and standard deviation percentage of 2.61%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=5), 'learning_rate': 0.5, 'n_estimators': 250} |
1031-17-2-1-5-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 1074 times representing a percentage of 14.50%. 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 0.161. to 18.610 and kurtosis values of 0.66 to 460.27. The fractal dimension analysis yields values ranging from -0.59 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 -0.80, maximum 1.00, mean 0.08, and standard deviation 0.55. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.40%, maximum percentage of 49.14%, average percentage of 35.32%, and standard deviation percentage of 16.02%.
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} |
1031-32-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 7330 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 7330 samples the target ground-truth class has changed 1409 times representing a percentage of 19.31%. 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 0.043. to 1.546 and kurtosis values of 0.08 to 6.31. The fractal dimension analysis yields values ranging from -0.56 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 0.12, and standard deviation 0.54. The count of numerical predictors with outliers is 14 with the minimum percentage of 0.00%, maximum percentage of 12.31%, average percentage of 5.60%, and standard deviation percentage of 4.35%.
Among the categorical predictors, the count of symbols ranges from 112 to 112 with a minimum entropy value 0.5211892203477435, maximum entropy 0.5211892203477435, mean 0.5211892203477435, 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} |
1016-4-1-3-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 0.99 showing a Balanced dataset. Among the 7109 samples the target ground-truth class has changed 258 times representing a percentage of 3.65%. 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 0.052. to 0.285 and kurtosis values of 0.06 to 0.65. The fractal dimension analysis yields values ranging from -0.49 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 -0.10, maximum 1.00, mean 0.58, and standard deviation 0.52. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.03%, maximum percentage of 0.28%, average percentage of 0.13%, and standard deviation percentage of 0.11%.
Among the categorical predictors, the count of symbols ranges from 9 to 66 with a minimum entropy value 1.5890156019255315, maximum entropy 5.223471366920816, mean 3.7122391558461962, and standard deviation 1.4335948409309873,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1030-6-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 30 times representing a percentage of 0.73%. 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 1.253. to 10.694 and kurtosis values of 0.51 to 250.71. The fractal dimension analysis yields values ranging from -0.63 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 -0.25, maximum 1.00, mean 0.60, and standard deviation 0.58. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 6.40%, average percentage of 1.28%, and standard deviation percentage of 2.86%.
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'} |
1031-57-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 6940 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 6940 samples the target ground-truth class has changed 267 times representing a percentage of 3.87%. 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 0.039. to 7.448 and kurtosis values of 5.24 to 102.39. 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 -0.96, maximum 1.00, mean 0.18, and standard deviation 0.56. The count of numerical predictors with outliers is 15 with the minimum percentage of 21.81%, maximum percentage of 21.81%, average percentage of 21.81%, and standard deviation percentage of 0.00%.
Among the categorical predictors, the count of symbols ranges from 11 to 11 with a minimum entropy value 0.05157430534889665, maximum entropy 0.05157430534889665, mean 0.05157430534889665, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 181.8181818181818, 'l1_ratio': 0.001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1029-14-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 1.50 showing a Unbalanced dataset. Among the 3503 samples the target ground-truth class has changed 34 times representing a percentage of 0.98%. 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 0.323. to 2.936 and kurtosis values of 0.89 to 14.60. 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 -0.32, maximum 1.00, mean 0.51, and standard deviation 0.64. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.28%, average percentage of 1.32%, and standard deviation percentage of 2.64%.
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'} |
1031-30-1-1-5-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 1582 times representing a percentage of 21.36%. 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 0.016. to 14.335 and kurtosis values of 0.01 to 288.23. 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 -0.94, maximum 1.00, mean 0.10, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.97%, maximum percentage of 16.32%, average percentage of 7.46%, and standard deviation percentage of 4.87%.
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} |
1031-30-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7470 samples and 14 features with 10 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 0.97 showing a Unbalanced dataset. Among the 7470 samples the target ground-truth class has changed 663 times representing a percentage of 8.92%. There are 14 features in the dataset with a ratio of numerical to categorical features of 2.5.
Among the numerical predictors, the series has 10 numerical features detected as Stationary out of the 10 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 0.200. to 1.297 and kurtosis values of 0.06 to 3.48. The fractal dimension analysis yields values ranging from -0.79 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 -0.21, maximum 1.00, mean 0.22, and standard deviation 0.45. The count of numerical predictors with outliers is 8 with the minimum percentage of 0.00%, maximum percentage of 3.89%, average percentage of 1.11%, and standard deviation percentage of 1.28%.
Among the categorical predictors, the count of symbols ranges from 55 to 74 with a minimum entropy value 4.488258678113898, maximum entropy 5.150405109874663, mean 4.771512683379417, and standard deviation 0.2626057179880522,
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} |
1031-10-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 6633 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 6633 samples the target ground-truth class has changed 240 times representing a percentage of 3.64%. 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 0.539. to 5.368 and kurtosis values of 3.30 to 31.54. The fractal dimension analysis yields values ranging from -0.22 to -0.06 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.40, maximum 1.00, mean 0.91, and standard deviation 0.18. The count of numerical predictors with outliers is 16 with the minimum percentage of 18.23%, maximum percentage of 18.23%, average percentage of 18.23%, and standard deviation 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': 50} |
1030-33-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 4 classes with entropy value 1.89 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 37 times representing a percentage of 0.90%. 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 0.723. to 11.665 and kurtosis values of 0.07 to 284.44. 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 -0.02, maximum 1.00, mean 0.67, and standard deviation 0.47. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.65%, maximum percentage of 4.97%, average percentage of 1.56%, and standard deviation percentage of 1.91%.
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'} |
3001-67.csv | A multivariate classification time-series dataset consists of 672 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 0 to 0 with mean 0.00
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 672 samples the target ground-truth class has changed 103 times representing a percentage of 15.61%. 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 0.932. to 0.932 and kurtosis values of 0.44 to 0.44. The fractal dimension analysis yields values ranging from -0.56 to -0.56 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} |
1030-199-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 67 times representing a percentage of 1.63%. There are 5 features in the dataset
Among the numerical predictors, the series has 4 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 0.458. to 19.158 and kurtosis values of 0.65 to 697.64. The fractal dimension analysis yields values ranging from -0.60 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 -0.21, maximum 1.00, mean 0.54, and standard deviation 0.49. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 8.10%, average percentage of 2.75%, and standard deviation percentage of 3.86%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 20, 'n_estimators': 100} |
1031-53-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 6726 samples and 11 features with 11 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 6726 samples the target ground-truth class has changed 640 times representing a percentage of 9.56%. There are 11 features in the dataset
Among the numerical predictors, the series has 11 numerical features detected as Stationary out of the 11 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 0.240. to 4.105 and kurtosis values of 1.98 to 25.26. The fractal dimension analysis yields values ranging from -0.40 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 -0.86, maximum 1.00, mean 0.13, and standard deviation 0.61. The count of numerical predictors with outliers is 11 with the minimum percentage of 11.42%, maximum percentage of 40.06%, average percentage of 23.38%, and standard deviation percentage of 10.67%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1031-8-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7915 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 7915 samples the target ground-truth class has changed 1653 times representing a percentage of 20.97%. 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. 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 0.025. to 12.070 and kurtosis values of 0.05 to 177.43. The fractal dimension analysis yields values ranging from -0.69 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 0.19, and standard deviation 0.46. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.28%, maximum percentage of 17.60%, average percentage of 10.88%, and standard deviation percentage of 5.02%.
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} |
1030-440-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 7 times representing a percentage of 0.17%. 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 0.692. to 31.056 and kurtosis values of 0.85 to 1508.06. The fractal dimension analysis yields values ranging from -0.67 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 0.03, 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 4.75%, average percentage of 0.95%, and standard deviation percentage of 2.13%.
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'} |
1031-43-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 4133 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 1.48 showing a Unbalanced dataset. Among the 4133 samples the target ground-truth class has changed 964 times representing a percentage of 23.52%. 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 0.023. to 10.239 and kurtosis values of 0.04 to 142.03. 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 -0.95, maximum 1.00, mean 0.13, and standard deviation 0.46. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.17%, maximum percentage of 13.27%, average percentage of 4.73%, and standard deviation percentage of 3.58%.
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} |
1031-13-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7400 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 7400 samples the target ground-truth class has changed 1625 times representing a percentage of 22.06%. 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 13.200 and kurtosis values of 0.10 to 215.89. The fractal dimension analysis yields values ranging from -0.66 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 0.50. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 12.71%, average percentage of 6.95%, and standard deviation percentage of 3.32%.
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': 10, 'reg_lambda': 0.2} |
3001-17.csv | A multivariate classification time-series dataset consists of 768 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 4 classes with entropy value 1.75 showing a Unbalanced dataset. Among the 768 samples the target ground-truth class has changed 753 times representing a percentage of 99.87%. 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. 2 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 76 The numerical predictors also exhibit skewness values ranging from 0.322. to 0.888 and kurtosis values of 0.03 to 0.49. The fractal dimension analysis yields values ranging from -1.59 to -1.41 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 0.44, and standard deviation 0.56. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 0.80%, average percentage of 0.40%, and standard deviation percentage of 0.56%.
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} |
1031-47-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7396 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 1.48 showing a Unbalanced dataset. Among the 7396 samples the target ground-truth class has changed 1407 times representing a percentage of 19.11%. 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 0.029. to 16.411 and kurtosis values of 0.18 to 363.47. The fractal dimension analysis yields values ranging from -0.71 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 -0.79, maximum 1.00, mean 0.09, and standard deviation 0.54. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.16%, maximum percentage of 16.18%, average percentage of 6.26%, and standard deviation percentage of 5.26%.
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} |
3001-49.csv | A multivariate classification time-series dataset consists of 1168 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 4 classes with entropy value 1.12 showing a Unbalanced dataset. Among the 1168 samples the target ground-truth class has changed 73 times representing a percentage of 6.33%. 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 0.011. to 1.110 and kurtosis values of 0.39 to 0.57. The fractal dimension analysis yields values ranging from -1.55 to -0.94 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.40, maximum 1.00, mean 0.30, and standard deviation 0.70. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 0.35%, average percentage of 0.17%, and standard deviation percentage of 0.25%.
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} |
1031-52-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 6399 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 6399 samples the target ground-truth class has changed 1339 times representing a percentage of 21.04%. 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 0.036. to 25.456 and kurtosis values of 0.03 to 916.26. 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 -0.95, 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 0.27%, maximum percentage of 11.14%, average percentage of 5.67%, and standard deviation percentage of 3.44%.
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} |
1031-20-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7619 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 7619 samples the target ground-truth class has changed 1254 times representing a percentage of 16.53%. 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 0.005. to 13.326 and kurtosis values of 0.55 to 217.35. The fractal dimension analysis yields values ranging from -0.63 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 -0.87, maximum 1.00, mean 0.22, and standard deviation 0.46. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.07%, maximum percentage of 34.54%, average percentage of 24.09%, and standard deviation percentage of 9.99%.
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} |
1031-38-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7702 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 7702 samples the target ground-truth class has changed 1549 times representing a percentage of 20.20%. 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 0.004. to 13.756 and kurtosis values of 0.03 to 240.27. The fractal dimension analysis yields values ranging from -0.70 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 -0.96, maximum 1.00, mean 0.10, and standard deviation 0.51. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.51%, maximum percentage of 16.13%, average percentage of 6.15%, and standard deviation percentage of 4.76%.
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} |
1031-46-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 6978 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 6978 samples the target ground-truth class has changed 1321 times representing a percentage of 19.02%. 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. 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 0.003. to 15.548 and kurtosis values of 0.06 to 310.16. The fractal dimension analysis yields values ranging from -0.69 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 -0.92, maximum 1.00, mean 0.09, and standard deviation 0.52. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.73%, maximum percentage of 18.32%, average percentage of 8.10%, and standard deviation percentage of 4.31%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 5, 'n_estimators': 50} |
1031-10-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 6666 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 6666 samples the target ground-truth class has changed 1148 times representing a percentage of 17.31%. 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 0.014. to 19.457 and kurtosis values of 0.01 to 513.07. 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 -0.96, 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 2.64%, maximum percentage of 28.92%, average percentage of 22.20%, and standard deviation percentage of 7.10%.
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} |
1016-5-3-4-classification.csv | A multivariate classification time-series dataset consists of 7108 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 1.00 showing a Balanced dataset. Among the 7108 samples the target ground-truth class has changed 342 times representing a percentage of 4.83%. 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 0.050. to 0.701 and kurtosis values of 0.09 to 1.12. The fractal dimension analysis yields values ranging from -0.40 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 -0.02, maximum 1.00, mean 0.61, and standard deviation 0.47. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.25%, maximum percentage of 2.05%, average percentage of 0.98%, and standard deviation percentage of 0.78%.
Among the categorical predictors, the count of symbols ranges from 36 to 65 with a minimum entropy value 1.221562774656684, maximum entropy 5.254735311861623, mean 3.856180208572821, and standard deviation 1.5981710029191343,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 500.0, 'l1_ratio': 0.00019999999999999998, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-35-1-1-1-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 1.43 showing a Unbalanced dataset. Among the 7548 samples the target ground-truth class has changed 1590 times representing a percentage of 21.16%. 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 0.008. to 11.198 and kurtosis values of 0.03 to 160.51. The fractal dimension analysis yields values ranging from -0.69 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 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.86%, maximum percentage of 13.53%, average percentage of 7.91%, and standard deviation percentage of 3.11%.
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} |
1031-54-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 6916 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 6916 samples the target ground-truth class has changed 1129 times representing a percentage of 16.41%. 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 0.000. to 1.705 and kurtosis values of 0.31 to 5.68. 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 -0.94, maximum 1.00, mean 0.14, and standard deviation 0.53. The count of numerical predictors with outliers is 15 with the minimum percentage of 27.32%, maximum percentage of 49.71%, average percentage of 41.29%, and standard deviation percentage of 7.95%.
Among the categorical predictors, the count of symbols ranges from 68 to 68 with a minimum entropy value 0.30007724292039956, maximum entropy 0.30007724292039956, mean 0.30007724292039956, 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} |
1031-46-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7361 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 7361 samples the target ground-truth class has changed 1420 times representing a percentage of 19.38%. 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 0.038. to 15.828 and kurtosis values of 0.01 to 369.46. 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 -0.95, maximum 1.00, mean 0.11, and standard deviation 0.51. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.30%, maximum percentage of 13.70%, average percentage of 6.87%, and standard deviation percentage of 4.00%.
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} |
1034-3-6-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 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 7963 samples the target ground-truth class has changed 440 times representing a percentage of 5.54%. 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 17 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 497,723,2654 The numerical predictors also exhibit skewness values ranging from 0.447. to 1.863 and kurtosis values of 0.09 to 2.77. The fractal dimension analysis yields values ranging from -0.77 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 0.46, and standard deviation 0.40. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.67%, maximum percentage of 13.34%, average percentage of 5.69%, and standard deviation percentage of 6.63%.
Among the categorical predictors, the count of symbols ranges from 66 to 66 with a minimum entropy value 5.203751789533447, maximum entropy 5.203751789533447, mean 5.203751789533447, 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': 10, 'reg_lambda': 0.2} |
1016-25-6-2-classification.csv | A multivariate classification time-series dataset consists of 7109 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 7109 samples the target ground-truth class has changed 223 times representing a percentage of 3.15%. 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 1 seasonality components detected in the numerical predictors. The top 1 common seasonality components are represented using sinusoidal waves. of periods 273 The numerical predictors also exhibit skewness values ranging from 0.058. to 0.777 and kurtosis values of 0.13 to 1.18. The fractal dimension analysis yields values ranging from -0.46 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 -0.07, maximum 1.00, mean 0.59, and standard deviation 0.51. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 2.50%, average percentage of 0.86%, and standard deviation percentage of 1.15%.
Among the categorical predictors, the count of symbols ranges from 29 to 66 with a minimum entropy value 1.721884985185291, maximum entropy 5.34201516617388, mean 4.066783487225347, and standard deviation 1.4172497467380596,
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 22.086008977892227, 'penalty': 'l1', 'solver': 'saga'} |
1031-48-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 7450 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 1.38 showing a Unbalanced dataset. Among the 7450 samples the target ground-truth class has changed 1351 times representing a percentage of 18.22%. 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. 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 0.029. to 12.656 and kurtosis values of 0.20 to 203.15. The fractal dimension analysis yields values ranging from -0.63 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 0.10, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.44%, maximum percentage of 23.50%, average percentage of 13.81%, and standard deviation percentage of 5.70%.
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} |
1030-179-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 0.98 showing a Balanced dataset. Among the 4140 samples the target ground-truth class has changed 15 times representing a percentage of 0.36%. 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 0.010. to 40.945 and kurtosis values of 0.85 to 2244.02. The fractal dimension analysis yields values ranging from -0.66 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 0.06, maximum 1.00, mean 0.70, and standard deviation 0.43. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 4.41%, average percentage of 0.88%, 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=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1030-175-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 1.53 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 43 times representing a percentage of 1.04%. 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 0.193. to 12.646 and kurtosis values of 0.52 to 316.60. The fractal dimension analysis yields values ranging from -0.59 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 -0.32, 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 5.89%, average percentage of 1.18%, and standard deviation percentage of 2.64%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 20, 'n_estimators': 200} |
1031-19-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 5436 samples and 15 features with 14 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 5436 samples the target ground-truth class has changed 1013 times representing a percentage of 18.75%. There are 15 features in the dataset with a ratio of numerical to categorical features of 14.0.
Among the numerical predictors, the series has 14 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 0.015. to 1.970 and kurtosis values of 0.07 to 8.63. The fractal dimension analysis yields values ranging from -0.48 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 14 with the minimum percentage of 0.02%, maximum percentage of 12.33%, average percentage of 5.05%, and standard deviation percentage of 4.04%.
Among the categorical predictors, the count of symbols ranges from 86 to 86 with a minimum entropy value 0.48373244612870575, maximum entropy 0.48373244612870575, mean 0.48373244612870575, 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} |
1020-63-4-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 1.35 showing a Unbalanced dataset. Among the 7012 samples the target ground-truth class has changed 572 times representing a percentage of 8.20%. 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 35 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 701,1402,1753 The numerical predictors also exhibit skewness values ranging from 0.350. to 2.356 and kurtosis values of 0.45 to 6.94. The fractal dimension analysis yields values ranging from -0.62 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 -0.78, maximum 1.00, mean 0.14, and standard deviation 0.52. The count of numerical predictors with outliers is 7 with the minimum percentage of 0.00%, maximum percentage of 9.01%, average percentage of 3.06%, and standard deviation percentage of 3.08%.
Among the categorical predictors, the count of symbols ranges from 17 to 74 with a minimum entropy value 0.541700370545832, maximum entropy 3.5736016190874658, mean 2.057650994816649, and standard deviation 1.515950624270817,
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} |
1031-1-3-1-4-classification.csv | A multivariate classification time-series dataset consists of 7335 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 7335 samples the target ground-truth class has changed 337 times representing a percentage of 4.62%. 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 1.173. to 8.089 and kurtosis values of 3.62 to 67.20. The fractal dimension analysis yields values ranging from -0.15 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 0.28, maximum 1.00, mean 0.87, and standard deviation 0.20. The count of numerical predictors with outliers is 16 with the minimum percentage of 20.74%, maximum percentage of 20.74%, average percentage of 20.74%, 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': 0.00055, 'penalty': 'elasticnet', 'solver': 'saga'} |
1016-16-2-2-classification.csv | A multivariate classification time-series dataset consists of 7246 samples and 4 features with 3 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 6436 to 6436 with mean 6436.0
The target column has 3 classes with entropy value 1.06 showing a Unbalanced dataset. Among the 7246 samples the target ground-truth class has changed 64 times representing a percentage of 8.10%. There are 4 features in the dataset with a ratio of numerical to categorical features of 3.0.
Among the numerical predictors, the series has 1 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 4 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 185,233,381 The numerical predictors also exhibit skewness values ranging from 0.007. to 0.221 and kurtosis values of 0.02 to 0.79. The fractal dimension analysis yields values ranging from -0.45 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 0.28, maximum 1.00, mean 0.68, and standard deviation 0.33. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 1.01%, average percentage of 0.34%, and standard deviation percentage of 0.58%.
Among the categorical predictors, the count of symbols ranges from 51 to 51 with a minimum entropy value 4.38034488262706, maximum entropy 4.38034488262706, mean 4.38034488262706, 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': 0.1, 'n_estimators': 50} |
1031-16-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7459 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 7459 samples the target ground-truth class has changed 1415 times representing a percentage of 19.06%. 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 14.340 and kurtosis values of 0.12 to 257.81. The fractal dimension analysis yields values ranging from -0.70 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 0.10, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.39%, maximum percentage of 13.43%, average percentage of 6.78%, and standard deviation percentage of 3.77%.
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} |
1030-337-classification.csv | A multivariate classification time-series dataset consists of 3131 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 1.44 showing a Unbalanced dataset. Among the 3131 samples the target ground-truth class has changed 55 times representing a percentage of 1.77%. 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 1.335. to 3.278 and kurtosis values of 1.58 to 17.74. The fractal dimension analysis yields values ranging from -0.65 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 0.01, maximum 1.00, mean 0.69, and standard deviation 0.45. The count of numerical predictors with outliers is 5 with the minimum percentage of 5.52%, maximum percentage of 10.98%, average percentage of 7.91%, and standard deviation percentage of 1.96%.
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'} |
1030-58-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 9 times representing a percentage of 0.22%. 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 0.409. to 2.516 and kurtosis values of 0.74 to 9.99. 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 -0.58, maximum 1.00, mean 0.50, and standard deviation 0.73. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 6.35%, average percentage of 1.27%, and standard deviation percentage of 2.84%.
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'} |
1034-3-2-classification.csv | A multivariate classification time-series dataset consists of 7964 samples and 6 features with 6 numerical and 0 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.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 7964 samples the target ground-truth class has changed 213 times representing a percentage of 2.68%. There are 6 features in the dataset
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. 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 0.001. to 0.640 and kurtosis values of 0.33 to 1.05. The fractal dimension analysis yields values ranging from -0.77 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 -0.29, maximum 1.00, mean 0.46, and standard deviation 0.37. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 3.55%, average percentage of 0.90%, and standard deviation percentage of 1.46%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=1), 'learning_rate': 0.1, 'n_estimators': 50} |
1016-5-3-2-classification.csv | A multivariate classification time-series dataset consists of 7108 samples and 8 features with 6 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 331 with mean 55.17 and standard deviation 135.13. Similarly, the missing values percentages for categorical features range from 331 to 331 with mean 331.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.98 showing a Balanced dataset. Among the 7108 samples the target ground-truth class has changed 326 times representing a percentage of 4.83%. There are 8 features in the dataset with a ratio of numerical to categorical features of 3.0.
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 2 seasonality components detected in the numerical predictors. The top 2 common seasonality components are represented using sinusoidal waves. of periods 56,150 The numerical predictors also exhibit skewness values ranging from 0.047. to 1.130 and kurtosis values of 0.13 to 0.78. The fractal dimension analysis yields values ranging from -0.57 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 0.34, and standard deviation 0.46. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.00%, maximum percentage of 1.69%, average percentage of 0.53%, and standard deviation percentage of 0.62%.
Among the categorical predictors, the count of symbols ranges from 58 to 66 with a minimum entropy value 1.3540500294755036, maximum entropy 4.151883466188715, mean 2.7529667478321094, and standard deviation 1.3989167183566056,
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'} |
1030-148-classification.csv | A multivariate classification time-series dataset consists of 3331 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 1.54 showing a Unbalanced dataset. Among the 3331 samples the target ground-truth class has changed 44 times representing a percentage of 1.33%. 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 0.237. to 6.042 and kurtosis values of 0.63 to 79.94. 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 -0.02, maximum 1.00, mean 0.68, and standard deviation 0.47. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 6.43%, average percentage of 1.29%, and standard deviation percentage of 2.87%.
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'} |
1031-9-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 6999 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 6999 samples the target ground-truth class has changed 1384 times representing a percentage of 19.87%. There are 16 features in the dataset
Among the numerical predictors, the series has 15 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 0.035. to 14.802 and kurtosis values of 0.02 to 280.11. 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 -0.93, maximum 1.00, mean 0.12, and standard deviation 0.54. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.78%, maximum percentage of 19.27%, average percentage of 9.56%, and standard deviation percentage of 5.75%.
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'} |
1030-401-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 1.35 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 24 times representing a percentage of 0.58%. 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 1.279. to 16.596 and kurtosis values of 0.69 to 585.78. 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 -0.22, 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 4.12%, maximum percentage of 5.41%, average percentage of 4.68%, and standard deviation percentage of 0.48%.
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'} |
1016-5-3-3-classification.csv | A multivariate classification time-series dataset consists of 7108 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 46 to 46 with mean 46.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 7108 samples the target ground-truth class has changed 394 times representing a percentage of 5.61%. 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 4 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 141,307,504 The numerical predictors also exhibit skewness values ranging from 0.246. to 0.699 and kurtosis values of 0.03 to 0.24. 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 0.05, maximum 1.00, mean 0.64, and standard deviation 0.44. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.16%, maximum percentage of 1.58%, average percentage of 0.76%, and standard deviation percentage of 0.62%.
Among the categorical predictors, the count of symbols ranges from 41 to 71 with a minimum entropy value 1.3440036231536787, maximum entropy 5.237445032089717, mean 3.9378745199369014, and standard deviation 1.5620622663780883,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 20, 'n_estimators': 400} |
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