dataset_name stringlengths 8 32 | series_description stringlengths 1.32k 2.25k | algorithm stringclasses 8 values | hyperparameters stringclasses 93 values |
|---|---|---|---|
1030-242-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 1.91 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 0.561. to 4.152 and kurtosis values of 0.79 to 28.78. 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 -0.34, maximum 1.00, mean 0.57, and standard deviation 0.62. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.94%, average percentage of 1.19%, and standard deviation percentage of 2.66%.
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'} |
1016-11-2-1-classification.csv | A multivariate classification time-series dataset consists of 7110 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 7110 samples the target ground-truth class has changed 304 times representing a percentage of 4.30%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.7142857142857143.
Among the numerical predictors, the series has 5 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 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 0.222. to 0.970 and kurtosis values of 0.09 to 0.99. The fractal dimension analysis yields values ranging from -0.47 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 -0.19, maximum 1.00, mean 0.44, and standard deviation 0.50. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 0.51%, average percentage of 0.13%, and standard deviation percentage of 0.22%.
Among the categorical predictors, the count of symbols ranges from 9 to 66 with a minimum entropy value 1.1627901447324558, maximum entropy 5.323887731530484, mean 3.493065353154523, and standard deviation 1.4830898029628865,
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'} |
1030-375-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 4140 samples the target ground-truth class has changed 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. 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 0.095. to 3.092 and kurtosis values of 0.84 to 22.76. 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 -0.15, maximum 1.00, mean 0.64, and standard deviation 0.53. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 4.72%, average percentage of 0.94%, and standard deviation percentage of 2.11%.
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} |
1031-47-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 7397 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 1.44 showing a Unbalanced dataset. Among the 7397 samples the target ground-truth class has changed 1362 times representing a percentage of 18.50%. 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 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 0.013. to 1.064 and kurtosis values of 0.11 to 1.82. 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 -0.75, maximum 1.00, mean 0.11, and standard deviation 0.55. The count of numerical predictors with outliers is 13 with the minimum percentage of 0.00%, maximum percentage of 20.33%, average percentage of 4.85%, and standard deviation percentage of 5.62%.
Among the categorical predictors, the count of symbols ranges from 100 to 100 with a minimum entropy value 0.396051162441303, maximum entropy 0.396051162441303, mean 0.396051162441303, 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} |
1031-7-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 6550 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 6550 samples the target ground-truth class has changed 282 times representing a percentage of 4.33%. 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 0.019. to 2.396 and kurtosis values of 2.67 to 7.91. The fractal dimension analysis yields values ranging from -0.21 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 0.51, maximum 1.00, mean 0.93, and standard deviation 0.12. The count of numerical predictors with outliers is 16 with the minimum percentage of 19.12%, maximum percentage of 19.12%, average percentage of 19.12%, 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} |
1031-54-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 6921 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 6921 samples the target ground-truth class has changed 1409 times representing a percentage of 20.46%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.032. to 21.477 and kurtosis values of 0.07 to 588.43. 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 -0.93, 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 0.00%, maximum percentage of 14.40%, average percentage of 5.91%, and standard deviation percentage of 4.67%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-40-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7372 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 7372 samples the target ground-truth class has changed 515 times representing a percentage of 7.02%. 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 1.780. to 22.635 and kurtosis values of 4.85 to 513.51. The fractal dimension analysis yields values ranging from -0.52 to -0.16 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 0.85, and standard deviation 0.28. The count of numerical predictors with outliers is 16 with the minimum percentage of 24.71%, maximum percentage of 24.71%, average percentage of 24.71%, 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} |
1031-87-1-2-classification.csv | A multivariate classification time-series dataset consists of 7305 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 1.41 showing a Unbalanced dataset. Among the 7305 samples the target ground-truth class has changed 807 times representing a percentage of 11.10%. 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. 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 0.021. to 2.021 and kurtosis values of 0.00 to 5.24. The fractal dimension analysis yields values ranging from -0.70 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 -0.94, maximum 1.00, mean 0.15, and standard deviation 0.53. The count of numerical predictors with outliers is 14 with the minimum percentage of 7.21%, maximum percentage of 39.72%, average percentage of 16.05%, and standard deviation percentage of 8.52%.
Among the categorical predictors, the count of symbols ranges from 38 to 38 with a minimum entropy value 0.21862734030575945, maximum entropy 0.21862734030575945, mean 0.21862734030575945, 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} |
1031-5-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 7547 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 7547 samples the target ground-truth class has changed 1358 times representing a percentage of 18.08%. 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 0.062. to 2.079 and kurtosis values of 0.09 to 4.97. The fractal dimension analysis yields values ranging from -0.63 to -0.10 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.91, maximum 1.00, mean 0.10, and standard deviation 0.55. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.76%, maximum percentage of 14.75%, average percentage of 6.30%, and standard deviation percentage of 4.61%.
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} |
1031-55-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 5284 samples and 16 features with 15 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 5284 samples the target ground-truth class has changed 936 times representing a percentage of 17.83%. 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. 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 0.003. to 1.165 and kurtosis values of 0.14 to 2.99. The fractal dimension analysis yields values ranging from -0.61 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 -0.91, maximum 1.00, mean 0.12, and standard deviation 0.54. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.10%, maximum percentage of 15.77%, average percentage of 5.09%, and standard deviation percentage of 4.69%.
Among the categorical predictors, the count of symbols ranges from 78 to 78 with a minimum entropy value 0.40476789203129004, maximum entropy 0.40476789203129004, mean 0.40476789203129004, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-31-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 7778 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 7778 samples the target ground-truth class has changed 1108 times representing a percentage of 14.31%. 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. 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 0.021. to 12.391 and kurtosis values of 0.09 to 200.92. The fractal dimension analysis yields values ranging from -0.59 to -0.15 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.92, maximum 1.00, mean 0.09, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 4.08%, maximum percentage of 34.81%, average percentage of 25.04%, and standard deviation percentage of 9.61%.
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} |
1020-63-3-classification.csv | A multivariate classification time-series dataset consists of 7012 samples and 11 features with 9 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.40 showing a Unbalanced dataset. Among the 7012 samples the target ground-truth class has changed 521 times representing a percentage of 7.47%. 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 16 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 350,637,701 The numerical predictors also exhibit skewness values ranging from 0.342. to 3.435 and kurtosis values of 0.09 to 16.90. 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 -0.82, maximum 1.00, mean 0.15, and standard deviation 0.50. The count of numerical predictors with outliers is 9 with the minimum percentage of 0.01%, maximum percentage of 7.85%, average percentage of 3.77%, and standard deviation percentage of 2.96%.
Among the categorical predictors, the count of symbols ranges from 17 to 59 with a minimum entropy value 0.5953887262341956, maximum entropy 3.6560631809142823, mean 2.1257259535742388, and standard deviation 1.5303372273400433,
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} |
1031-52-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 6768 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 6768 samples the target ground-truth class has changed 1288 times representing a percentage of 19.13%. 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 0.115. to 9.701 and kurtosis values of 0.26 to 108.95. The fractal dimension analysis yields values ranging from -0.68 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.82, maximum 1.00, mean 0.12, and standard deviation 0.47. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 14.39%, average percentage of 7.72%, and standard deviation percentage of 3.47%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1028-37-classification.csv | A multivariate classification time-series dataset consists of 6231 samples and 8 features with 8 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 6231 samples the target ground-truth class has changed 49 times representing a percentage of 0.79%. There are 8 features in the dataset
Among the numerical predictors, the series has 2 numerical features detected as Stationary out of the 8 numerical features using the dickey-fuller test and the rest are Unstationary. 8 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 0.823. to 5.814 and kurtosis values of 0.20 to 122.80. The fractal dimension analysis yields values ranging from -0.65 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 -0.13, maximum 1.00, mean 0.65, and standard deviation 0.46. The count of numerical predictors with outliers is 8 with the minimum percentage of 1.27%, maximum percentage of 4.09%, average percentage of 1.71%, and standard deviation percentage of 0.98%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 40, 'n_estimators': 400} |
1016-21-1-2-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 47 to 47 with mean 47.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 7212 samples the target ground-truth class has changed 392 times representing a percentage of 5.50%. 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 170,325,421 The numerical predictors also exhibit skewness values ranging from 0.148. to 0.395 and kurtosis values of 0.25 to 0.45. The fractal dimension analysis yields values ranging from -0.39 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.07, maximum 1.00, mean 0.64, and standard deviation 0.43. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.03%, maximum percentage of 0.36%, average percentage of 0.16%, and standard deviation percentage of 0.14%.
Among the categorical predictors, the count of symbols ranges from 39 to 72 with a minimum entropy value 1.552471720384121, maximum entropy 5.357685413985215, mean 3.9541270675566595, and standard deviation 1.4743332814730086,
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} |
2011-2.csv | A multivariate classification time-series dataset consists of 28051 samples and 8 features with 2 numerical and 6 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 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 10 classes with entropy value 1.51 showing a Unbalanced dataset. Among the 28051 samples the target ground-truth class has changed 4102 times representing a percentage of 14.64%. There are 8 features in the dataset with a ratio of numerical to categorical features of 0.3333333333333333.
Among the numerical predictors, the series has 2 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 21 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 2805,4675,5610 The numerical predictors also exhibit skewness values ranging from 0.085. to 0.184 and kurtosis values of 0.51 to 1.29. The fractal dimension analysis yields values ranging from -0.70 to -0.15 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.18, maximum 1.00, mean 0.59, and standard deviation 0.41. The count of numerical predictors with outliers is 0 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%, and standard deviation percentage of 0.00%.
Among the categorical predictors, the count of symbols ranges from 4 to 94 with a minimum entropy value 0.0799607109424571, maximum entropy 6.085852897519147, mean 2.725218791744267, and standard deviation 2.1700624154944133,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-6-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 6713 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 6713 samples the target ground-truth class has changed 592 times representing a percentage of 8.86%. 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 0.133. to 2.011 and kurtosis values of 0.07 to 5.21. The fractal dimension analysis yields values ranging from -0.76 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 -0.94, maximum 1.00, mean 0.12, and standard deviation 0.53. The count of numerical predictors with outliers is 10 with the minimum percentage of 0.00%, maximum percentage of 9.01%, average percentage of 2.49%, and standard deviation percentage of 3.43%.
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} |
1030-478-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 25 times representing a percentage of 0.61%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.404. to 11.315 and kurtosis values of 0.89 to 279.70. The fractal dimension analysis yields values ranging from -0.62 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 0.10, maximum 1.00, mean 0.72, and standard deviation 0.41. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 6.62%, average percentage of 1.32%, and standard deviation percentage of 2.96%.
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} |
1020-26-1-classification.csv | A multivariate classification time-series dataset consists of 7008 samples and 11 features with 9 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.44 showing a Unbalanced dataset. Among the 7008 samples the target ground-truth class has changed 913 times representing a percentage of 13.09%. 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 9 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 32 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 637,778,2336 The numerical predictors also exhibit skewness values ranging from 0.106. to 2.727 and kurtosis values of 0.20 to 9.36. The fractal dimension analysis yields values ranging from -0.72 to -0.15 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.72, maximum 1.00, mean 0.14, and standard deviation 0.47. The count of numerical predictors with outliers is 9 with the minimum percentage of 0.23%, maximum percentage of 11.61%, average percentage of 4.73%, and standard deviation percentage of 3.88%.
Among the categorical predictors, the count of symbols ranges from 17 to 62 with a minimum entropy value 0.46498253683438295, maximum entropy 3.7404130066257366, mean 2.10269777173006, and standard deviation 1.637715234895677,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1030-47-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 1.95 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 53 times representing a percentage of 1.29%. 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 0.259. to 4.952 and kurtosis values of 0.86 to 76.95. The fractal dimension analysis yields values ranging from -0.65 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 -0.51, maximum 1.00, mean 0.52, and standard deviation 0.70. 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'} |
1031-60-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 6576 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 6576 samples the target ground-truth class has changed 1384 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. 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 0.099. to 18.499 and kurtosis values of 0.01 to 526.63. The fractal dimension analysis yields values ranging from -0.66 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.10, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.08%, maximum percentage of 13.71%, average percentage of 5.49%, and standard deviation percentage of 3.54%.
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} |
3001-3.csv | A multivariate classification time-series dataset consists of 192 samples and 2 features with 2 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 192 samples the target ground-truth class has changed 2 times representing a percentage of 1.11%. 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 0.042. to 0.075 and kurtosis values of 1.93 to 1.96. The fractal dimension analysis yields values ranging from -0.81 to -0.67 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.91, maximum 1.00, mean 0.95, and standard deviation 0.05. The count of numerical predictors with outliers is 0 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 333.3333333333333, 'l1_ratio': 0.0007999999999999999, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-29-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 5069 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 5069 samples the target ground-truth class has changed 253 times representing a percentage of 5.02%. 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 0.108. to 15.940 and kurtosis values of 3.44 to 304.72. 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 -0.89, maximum 1.00, mean 0.15, and standard deviation 0.56. The count of numerical predictors with outliers is 16 with the minimum percentage of 27.55%, maximum percentage of 27.55%, average percentage of 27.55%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 100.0, 'l1_ratio': 0.001, 'penalty': 'elasticnet', 'solver': 'saga'} |
3001-89.csv | A multivariate classification time-series dataset consists of 42048 samples and 3 features with 3 numerical and 0 categorical features. Each instance has a window length of 6. The dataset has a sampling rate of 10.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 3 to 6 with mean 4.67 and standard deviation 1.53.
The target column has 4 classes with entropy value 1.03 showing a Unbalanced dataset. Among the 42048 samples the target ground-truth class has changed 12 times representing a percentage of 0.03%. There are 3 features in the dataset
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. 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 0.015. to 0.940 and kurtosis values of 0.34 to 0.52. The fractal dimension analysis yields values ranging from -0.97 to -0.95 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.51, maximum 1.00, mean 0.76, and standard deviation 0.22. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 1.25%, average percentage of 0.42%, and standard deviation percentage of 0.72%.
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} |
1020-59-1-classification.csv | A multivariate classification time-series dataset consists of 7008 samples and 11 features with 9 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.44 showing a Unbalanced dataset. Among the 7008 samples the target ground-truth class has changed 861 times representing a percentage of 12.35%. 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 9 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 29 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 637,700,2336 The numerical predictors also exhibit skewness values ranging from 0.036. to 2.446 and kurtosis values of 0.81 to 7.06. The fractal dimension analysis yields values ranging from -0.74 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 -0.76, maximum 1.00, mean 0.15, and standard deviation 0.45. The count of numerical predictors with outliers is 6 with the minimum percentage of 0.00%, maximum percentage of 9.23%, average percentage of 3.10%, and standard deviation percentage of 3.11%.
Among the categorical predictors, the count of symbols ranges from 17 to 56 with a minimum entropy value 0.46105655378550475, maximum entropy 4.018254556243753, mean 2.239655555014629, and standard deviation 1.7785990012291242,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1031-36-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 7547 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 7547 samples the target ground-truth class has changed 479 times representing a percentage of 6.38%. 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. 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 0.889. to 1.697 and kurtosis values of 1.07 to 3.42. The fractal dimension analysis yields values ranging from -0.21 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 0.37, maximum 1.00, mean 0.92, and standard deviation 0.17. The count of numerical predictors with outliers is 16 with the minimum percentage of 30.65%, maximum percentage of 30.65%, average percentage of 30.65%, 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} |
1029-12-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 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 3503 samples the target ground-truth class has changed 13 times representing a percentage of 0.37%. 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 0.658. to 1.648 and kurtosis values of 0.62 to 4.38. The fractal dimension analysis yields values ranging from -0.58 to -0.28 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 0.40, and standard deviation 0.77. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 3.64%, average percentage of 0.91%, and standard deviation percentage of 1.82%.
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'} |
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