dataset_name stringlengths 8 32 | series_description stringlengths 1.32k 2.25k | algorithm stringclasses 8 values | hyperparameters stringclasses 93 values |
|---|---|---|---|
1031-12-3-1-2-classification.csv | A multivariate classification time-series dataset consists of 6440 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 6440 samples the target ground-truth class has changed 1392 times representing a percentage of 21.73%. 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 0.037. to 11.465 and kurtosis values of 0.20 to 165.72. The fractal dimension analysis yields values ranging from -0.65 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 -0.97, maximum 1.00, mean 0.07, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.04%, maximum percentage of 15.49%, average percentage of 8.49%, and standard deviation percentage of 4.58%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 5, 'n_estimators': 30} |
1031-10-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 6667 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 6667 samples the target ground-truth class has changed 1265 times representing a percentage of 19.07%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.016. to 32.317 and kurtosis values of 0.11 to 1560.91. The fractal dimension analysis yields values ranging from -0.72 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.91, maximum 1.00, mean 0.10, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.37%, maximum percentage of 36.32%, average percentage of 25.56%, and standard deviation percentage of 11.07%.
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} |
1031-8-2-1-6-classification.csv | A multivariate classification time-series dataset consists of 7916 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 1.46 showing a Unbalanced dataset. Among the 7916 samples the target ground-truth class has changed 1672 times representing a percentage of 21.21%. 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 0.035. to 13.513 and kurtosis values of 0.02 to 216.11. 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 -0.96, maximum 1.00, mean 0.11, and standard deviation 0.53. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.94%, maximum percentage of 15.86%, average percentage of 6.36%, and standard deviation percentage of 5.32%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1016-4-2-1-classification.csv | A multivariate classification time-series dataset consists of 7110 samples and 12 features with 4 numerical and 8 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 7110 samples the target ground-truth class has changed 292 times representing a percentage of 4.13%. 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 0.071. to 0.596 and kurtosis values of 0.13 to 0.57. The fractal dimension analysis yields values ranging from -0.50 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.20, maximum 1.00, mean 0.70, and standard deviation 0.37. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 0.75%, average percentage of 0.23%, and standard deviation percentage of 0.35%.
Among the categorical predictors, the count of symbols ranges from 9 to 59 with a minimum entropy value 1.4141173825298572, maximum entropy 5.223696311994492, mean 3.691657352941407, and standard deviation 1.4453070267380932,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-53-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 3705 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 3705 samples the target ground-truth class has changed 707 times representing a percentage of 19.26%. 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 0.086. to 15.185 and kurtosis values of 0.08 to 299.31. The fractal dimension analysis yields values ranging from -0.67 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 -0.91, maximum 1.00, mean 0.08, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.17%, maximum percentage of 12.69%, average percentage of 6.98%, and standard deviation percentage of 3.41%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-33-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7440 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.41 showing a Unbalanced dataset. Among the 7440 samples the target ground-truth class has changed 1532 times representing a percentage of 20.69%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.046. to 16.438 and kurtosis values of 0.24 to 354.12. The fractal dimension analysis yields values ranging from -0.67 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 0.11, and standard deviation 0.48. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 15.42%, average percentage of 8.82%, and standard deviation percentage of 4.08%.
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} |
1031-18-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7393 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.41 showing a Unbalanced dataset. Among the 7393 samples the target ground-truth class has changed 1197 times representing a percentage of 16.27%. 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 0.076. to 23.916 and kurtosis values of 0.17 to 917.97. The fractal dimension analysis yields values ranging from -0.63 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.81, maximum 1.00, mean 0.14, and standard deviation 0.45. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 11.22%, average percentage of 6.37%, and standard deviation percentage of 3.25%.
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} |
1031-34-1-1-3-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 1.37 showing a Unbalanced dataset. Among the 7678 samples the target ground-truth class has changed 1585 times representing a percentage of 20.74%. 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 0.055. to 18.715 and kurtosis values of 0.62 to 543.01. The fractal dimension analysis yields values ranging from -0.65 to -0.17 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 0.45. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.45%, maximum percentage of 45.26%, average percentage of 30.49%, and standard deviation percentage of 14.29%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1031-28-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 7325 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 7325 samples the target ground-truth class has changed 1355 times representing a percentage of 18.58%. 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 15.283 and kurtosis values of 0.54 to 293.11. 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 -0.94, 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 3.10%, maximum percentage of 32.11%, average percentage of 26.52%, and standard deviation percentage of 7.78%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-40-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 6856 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 6856 samples the target ground-truth class has changed 1502 times representing a percentage of 22.02%. 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 0.052. to 12.276 and kurtosis values of 0.00 to 200.14. 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 0.09, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.25%, maximum percentage of 14.45%, average percentage of 7.55%, and standard deviation percentage of 3.59%.
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} |
3001-16.csv | A multivariate classification time-series dataset consists of 504 samples and 1 features with 1 numerical and 0 categorical features. Each instance has a window length of 3. The dataset has a sampling rate of 480.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00
The target column has 4 classes with entropy value 1.27 showing a Unbalanced dataset. Among the 504 samples the target ground-truth class has changed 329 times representing a percentage of 66.87%. There are 1 features in the dataset
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 1 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.021. to 0.021 and kurtosis values of 0.78 to 0.78. The fractal dimension analysis yields values ranging from -1.38 to -1.38 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 1.00, maximum 1.00, mean 1.00, and standard deviation 0.00. The count of numerical predictors with outliers is 0 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-35-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7607 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 7607 samples the target ground-truth class has changed 1614 times representing a percentage of 21.31%. 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 0.001. to 12.317 and kurtosis values of 0.07 to 189.52. 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 -0.98, 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 13.18%, average percentage of 6.64%, and standard deviation percentage of 4.39%.
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} |
1030-227-classification.csv | A multivariate classification time-series dataset consists of 3796 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.55 showing a Unbalanced dataset. Among the 3796 samples the target ground-truth class has changed 10 times representing a percentage of 0.26%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.749. to 13.509 and kurtosis values of 0.26 to 359.09. 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 -0.28, maximum 1.00, mean 0.59, and standard deviation 0.59. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.26%, maximum percentage of 5.32%, average percentage of 1.28%, and standard deviation percentage of 2.26%.
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'} |
1031-48-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7456 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.41 showing a Unbalanced dataset. Among the 7456 samples the target ground-truth class has changed 1520 times representing a percentage of 20.48%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 16.757 and kurtosis values of 0.17 to 406.66. 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 -0.93, 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 3.22%, maximum percentage of 18.16%, average percentage of 10.42%, and standard deviation percentage of 5.10%.
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} |
1031-45-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7483 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 7483 samples the target ground-truth class has changed 1449 times representing a percentage of 19.45%. 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 0.029. to 14.473 and kurtosis values of 0.03 to 280.77. The fractal dimension analysis yields values ranging from -0.64 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 -0.72, maximum 1.00, mean 0.17, and standard deviation 0.44. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 17.95%, average percentage of 7.75%, and standard deviation percentage of 5.17%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1029-11-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 1.00 showing a Balanced dataset. Among the 3503 samples the target ground-truth class has changed 3 times representing a percentage of 0.09%. 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 0.234. to 5.198 and kurtosis values of 1.29 to 72.22. The fractal dimension analysis yields values ranging from -0.60 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 -0.30, maximum 1.00, mean 0.52, and standard deviation 0.63. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 4.45%, average percentage of 1.11%, and standard deviation percentage of 2.22%.
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'} |
1030-410-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 11 times representing a percentage of 0.27%. 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 1.049. to 3.896 and kurtosis values of 0.24 to 26.18. 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 -0.16, maximum 1.00, mean 0.63, and standard deviation 0.54. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.02%, maximum percentage of 6.11%, average percentage of 1.26%, and standard deviation percentage of 2.71%.
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'} |
1031-28-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7324 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 7324 samples the target ground-truth class has changed 1502 times representing a percentage of 20.60%. 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 0.085. to 11.969 and kurtosis values of 0.07 to 182.73. 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 -0.97, 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 1.17%, maximum percentage of 13.76%, average percentage of 6.89%, and standard deviation percentage of 3.90%.
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} |
1031-5-3-1-5-classification.csv | A multivariate classification time-series dataset consists of 5829 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 1.38 showing a Unbalanced dataset. Among the 5829 samples the target ground-truth class has changed 1000 times representing a percentage of 17.26%. 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 0.058. to 1.516 and kurtosis values of 0.20 to 2.45. The fractal dimension analysis yields values ranging from -0.62 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 -0.88, maximum 1.00, mean 0.13, and standard deviation 0.53. The count of numerical predictors with outliers is 12 with the minimum percentage of 0.00%, maximum percentage of 16.69%, average percentage of 5.92%, and standard deviation percentage of 4.78%.
Among the categorical predictors, the count of symbols ranges from 58 to 58 with a minimum entropy value 0.24252336387947288, maximum entropy 0.24252336387947288, mean 0.24252336387947288, 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} |
1031-35-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 7608 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 7608 samples the target ground-truth class has changed 1611 times representing a percentage of 21.27%. 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. 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 0.009. to 10.373 and kurtosis values of 0.20 to 137.21. 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 0.11, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.03%, maximum percentage of 12.50%, average percentage of 6.57%, and standard deviation percentage of 3.71%.
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} |
1016-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7385 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 7385 samples the target ground-truth class has changed 264 times representing a percentage of 3.59%. 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 307 The numerical predictors also exhibit skewness values ranging from 0.165. to 0.468 and kurtosis values of 0.44 to 0.69. The fractal dimension analysis yields values ranging from -0.46 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 0.06, 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 0.01%, maximum percentage of 0.90%, average percentage of 0.26%, and standard deviation percentage of 0.43%.
Among the categorical predictors, the count of symbols ranges from 36 to 61 with a minimum entropy value 1.4078035633237453, maximum entropy 5.316204486484947, mean 4.158742749583427, and standard deviation 1.5950857844606092,
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 78.07278248131658, 'penalty': 'l1', 'solver': 'saga'} |
1016-13-3-1-classification.csv | A multivariate classification time-series dataset consists of 6656 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 1.51 showing a Unbalanced dataset. Among the 6656 samples the target ground-truth class has changed 440 times representing a percentage of 6.64%. 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. 5 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 93,105,170 The numerical predictors also exhibit skewness values ranging from 0.150. to 0.696 and kurtosis values of 0.03 to 0.49. 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 -0.21, maximum 1.00, mean 0.48, and standard deviation 0.51. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.05%, maximum percentage of 1.06%, average percentage of 0.40%, and standard deviation percentage of 0.46%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 40, 'n_estimators': 200} |
1031-52-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 6779 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 6779 samples the target ground-truth class has changed 1354 times representing a percentage of 20.07%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.008. to 15.222 and kurtosis values of 0.17 to 298.92. 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 -0.87, maximum 1.00, mean 0.08, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.02%, maximum percentage of 14.23%, average percentage of 9.80%, and standard deviation percentage of 4.11%.
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} |
3001-100.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 1 times representing a percentage of 0.15%. 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 0.906. to 0.906 and kurtosis values of 0.29 to 0.29. The fractal dimension analysis yields values ranging from -1.51 to -1.51 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': 250} |
1016-13-5-3-classification.csv | A multivariate classification time-series dataset consists of 6594 samples and 8 features with 5 numerical and 3 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 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 6594 samples the target ground-truth class has changed 197 times representing a percentage of 3.00%. There are 8 features in the dataset with a ratio of numerical to categorical features of 1.6666666666666667.
Among the numerical predictors, the series has 5 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.073. to 0.629 and kurtosis values of 0.07 to 0.70. The fractal dimension analysis yields values ranging from -0.42 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.25, maximum 1.00, mean 0.52, and standard deviation 0.44. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 0.81%, average percentage of 0.25%, and standard deviation percentage of 0.34%.
Among the categorical predictors, the count of symbols ranges from 39 to 50 with a minimum entropy value 1.234344711534946, maximum entropy 5.041412280103284, mean 3.6203295604359123, and standard deviation 1.6973936799616938,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 40, 'n_estimators': 250} |
1031-8-1-1-5-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 1.49 showing a Unbalanced dataset. Among the 7915 samples the target ground-truth class has changed 1725 times representing a percentage of 21.89%. 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 0.058. to 25.793 and kurtosis values of 0.14 to 994.70. The fractal dimension analysis yields values ranging from -0.74 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 -0.85, maximum 1.00, mean 0.20, and standard deviation 0.46. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.04%, maximum percentage of 14.93%, average percentage of 5.67%, and standard deviation percentage of 4.53%.
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} |
1016-25-6-3-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 32 to 32 with mean 32.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7109 samples the target ground-truth class has changed 229 times representing a percentage of 3.25%. 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 128 The numerical predictors also exhibit skewness values ranging from 0.227. to 0.778 and kurtosis values of 0.06 to 0.76. The fractal dimension analysis yields values ranging from -0.46 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.10, maximum 1.00, mean 0.66, and standard deviation 0.43. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 1.90%, average percentage of 0.78%, and standard deviation percentage of 0.81%.
Among the categorical predictors, the count of symbols ranges from 42 to 63 with a minimum entropy value 1.7011847435770864, maximum entropy 5.253610310222936, mean 4.08332190248886, and standard deviation 1.4074750367545354,
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 1486.1362312349506, 'penalty': 'l1', 'solver': 'saga'} |
1031-16-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 7460 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 7460 samples the target ground-truth class has changed 1407 times representing a percentage of 18.95%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.096. to 17.138 and kurtosis values of 0.06 to 391.20. The fractal dimension analysis yields values ranging from -0.67 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.90, maximum 1.00, mean 0.09, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.13%, maximum percentage of 13.39%, average percentage of 7.72%, and standard deviation percentage of 3.28%.
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} |
1030-238-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 188 times representing a percentage of 4.56%. 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. 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 0.106. to 1.805 and kurtosis values of 0.04 to 5.78. The fractal dimension analysis yields values ranging from -0.64 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 -0.44, maximum 1.00, mean 0.51, and standard deviation 0.63. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 4.03%, average percentage of 1.07%, and standard deviation percentage of 1.67%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=1), 'learning_rate': 1.0, 'n_estimators': 50} |
1020-38-4-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 1.44 showing a Unbalanced dataset. Among the 7012 samples the target ground-truth class has changed 984 times representing a percentage of 14.10%. There are 11 features in the dataset with a ratio of numerical to categorical features of 4.5.
Among the numerical predictors, the series has 8 numerical features detected as Stationary out of the 9 numerical features using the dickey-fuller test and the rest are Unstationary. 9 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 41 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 1001,1001,1402 The numerical predictors also exhibit skewness values ranging from 0.405. to 3.244 and kurtosis values of 0.40 to 13.23. The fractal dimension analysis yields values ranging from -0.73 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.77, maximum 1.00, mean 0.15, and standard deviation 0.47. The count of numerical predictors with outliers is 7 with the minimum percentage of 0.00%, maximum percentage of 11.34%, average percentage of 4.40%, and standard deviation percentage of 3.79%.
Among the categorical predictors, the count of symbols ranges from 17 to 75 with a minimum entropy value 0.5518450227716487, maximum entropy 3.9392277468431582, mean 2.2455363848074033, and standard deviation 1.6936913620357548,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-13-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 4933 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 4933 samples the target ground-truth class has changed 993 times representing a percentage of 20.27%. 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. 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 0.009. to 13.276 and kurtosis values of 0.16 to 221.73. 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 -0.91, maximum 1.00, mean 0.09, and standard deviation 0.49. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 11.61%, average percentage of 6.02%, and standard deviation percentage of 3.19%.
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} |
1020-35-3-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 1.43 showing a Unbalanced dataset. Among the 7012 samples the target ground-truth class has changed 755 times representing a percentage of 10.82%. 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 35 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 1402,1753,2337 The numerical predictors also exhibit skewness values ranging from 0.304. to 4.896 and kurtosis values of 0.43 to 53.32. The fractal dimension analysis yields values ranging from -0.63 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.81, maximum 1.00, mean 0.14, and standard deviation 0.49. The count of numerical predictors with outliers is 7 with the minimum percentage of 0.00%, maximum percentage of 10.76%, average percentage of 3.07%, and standard deviation percentage of 3.52%.
Among the categorical predictors, the count of symbols ranges from 17 to 75 with a minimum entropy value 0.6433163799985939, maximum entropy 3.9107238595228773, mean 2.277020119760736, and standard deviation 1.6337037397621417,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-46-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 7228 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 7228 samples the target ground-truth class has changed 1367 times representing a percentage of 19.00%. 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 0.013. to 18.275 and kurtosis values of 0.03 to 442.96. The fractal dimension analysis yields values ranging from -0.63 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.92, maximum 1.00, mean 0.12, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.33%, maximum percentage of 13.36%, average percentage of 6.49%, and standard deviation percentage of 3.91%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-15-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 7005 samples and 15 features with 15 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.36 showing a Unbalanced dataset. Among the 7005 samples the target ground-truth class has changed 1165 times representing a percentage of 16.71%. 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. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.008. to 3.279 and kurtosis values of 0.34 to 19.54. 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 -0.96, 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 12.70%, maximum percentage of 36.68%, average percentage of 28.78%, and standard deviation percentage of 7.69%.
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} |
1031-19-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7715 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 7715 samples the target ground-truth class has changed 771 times representing a percentage of 10.04%. 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 0.019. to 22.947 and kurtosis values of 2.38 to 722.18. 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 -0.64, maximum 1.00, mean 0.18, and standard deviation 0.47. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.49%, maximum percentage of 40.76%, average percentage of 33.26%, and standard deviation percentage of 13.46%.
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} |
1031-36-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7660 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 7660 samples the target ground-truth class has changed 306 times representing a percentage of 4.01%. 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 0.450. to 50.258 and kurtosis values of 3.43 to 2529.25. The fractal dimension analysis yields values ranging from -0.96 to -0.17 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 0.83, and standard deviation 0.32. The count of numerical predictors with outliers is 16 with the minimum percentage of 17.27%, maximum percentage of 17.27%, average percentage of 17.27%, 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': 1.0, 'n_estimators': 50} |
1016-22-2-4-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 1 to 1 with mean 1.0 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 375 times representing a percentage of 5.30%. 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 3 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 89,134,197 The numerical predictors also exhibit skewness values ranging from 0.143. to 0.368 and kurtosis values of 0.07 to 0.31. The fractal dimension analysis yields values ranging from -0.41 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.08, maximum 1.00, mean 0.59, and standard deviation 0.50. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.23%, maximum percentage of 0.82%, average percentage of 0.56%, and standard deviation percentage of 0.25%.
Among the categorical predictors, the count of symbols ranges from 35 to 68 with a minimum entropy value 1.4505186121335742, maximum entropy 5.308967814434994, mean 3.8978075298269097, and standard deviation 1.5158379697699191,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 142.85714285714286, 'l1_ratio': 0.00019999999999999998, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-50-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 6768 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 6768 samples the target ground-truth class has changed 369 times representing a percentage of 5.48%. 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 0.005. to 3.084 and kurtosis values of 5.05 to 27.28. 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 -0.94, maximum 1.00, mean 0.10, and standard deviation 0.52. The count of numerical predictors with outliers is 15 with the minimum percentage of 22.57%, maximum percentage of 22.57%, average percentage of 22.57%, and standard deviation percentage of 0.00%.
Among the categorical predictors, the count of symbols ranges from 37 to 37 with a minimum entropy value 0.12347522009435763, maximum entropy 0.12347522009435763, mean 0.12347522009435763, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-30-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7228 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.41 showing a Unbalanced dataset. Among the 7228 samples the target ground-truth class has changed 1560 times representing a percentage of 21.68%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.093. to 11.003 and kurtosis values of 0.29 to 148.71. The fractal dimension analysis yields values ranging from -0.65 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.96, maximum 1.00, mean 0.12, and standard deviation 0.47. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.71%, maximum percentage of 23.34%, average percentage of 11.16%, and standard deviation percentage of 4.87%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 5, 'n_estimators': 30} |
1031-17-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7401 samples and 13 features with 13 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.40 showing a Unbalanced dataset. Among the 7401 samples the target ground-truth class has changed 1397 times representing a percentage of 18.96%. There are 13 features in the dataset
Among the numerical predictors, the series has 13 numerical features detected as Stationary out of the 13 numerical features using the dickey-fuller test and the rest are Unstationary. 11 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.058. to 15.293 and kurtosis values of 0.04 to 284.28. The fractal dimension analysis yields values ranging from -0.70 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.89, maximum 1.00, mean 0.14, and standard deviation 0.58. The count of numerical predictors with outliers is 13 with the minimum percentage of 3.05%, maximum percentage of 17.82%, average percentage of 10.60%, and standard deviation percentage of 4.91%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 5, 'n_estimators': 50} |
1031-31-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7768 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 7768 samples the target ground-truth class has changed 1187 times representing a percentage of 15.35%. 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 0.128. to 12.552 and kurtosis values of 0.08 to 240.81. The fractal dimension analysis yields values ranging from -0.61 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 -0.73, maximum 1.00, mean 0.18, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.97%, maximum percentage of 18.35%, average percentage of 11.93%, and standard deviation percentage of 4.73%.
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} |
1030-468-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 1.50 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 22 times representing a percentage of 0.53%. 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 0.792. to 8.161 and kurtosis values of 0.80 to 129.83. The fractal dimension analysis yields values ranging from -0.59 to -0.33 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.41, maximum 1.00, mean 0.55, and standard deviation 0.65. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 7.52%, average percentage of 1.50%, and standard deviation percentage of 3.36%.
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'} |
1016-23-2-2-classification.csv | A multivariate classification time-series dataset consists of 7871 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 0.97 showing a Unbalanced dataset. Among the 7871 samples the target ground-truth class has changed 274 times representing a percentage of 3.50%. 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 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.144. to 0.698 and kurtosis values of 0.04 to 1.11. The fractal dimension analysis yields values ranging from -0.36 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.14, maximum 1.00, mean 0.56, and standard deviation 0.54. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 2.46%, average percentage of 1.00%, and standard deviation percentage of 1.05%.
Among the categorical predictors, the count of symbols ranges from 29 to 76 with a minimum entropy value 1.1114092571382648, maximum entropy 5.271431325010087, mean 3.8099428253826173, and standard deviation 1.6417534854159495,
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} |
2001.csv | A multivariate classification time-series dataset consists of 2632 samples and 24 features with 24 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 4 classes with entropy value 1.18 showing a Unbalanced dataset. Among the 2632 samples the target ground-truth class has changed 706 times representing a percentage of 27.17%. There are 24 features in the dataset
Among the numerical predictors, the series has 21 numerical features detected as Stationary out of the 24 numerical features using the dickey-fuller test and the rest are Unstationary. 23 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 46 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 329,526,877 The numerical predictors also exhibit skewness values ranging from 0.024. to 3.475 and kurtosis values of 0.02 to 14.56. The fractal dimension analysis yields values ranging from -1.01 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 -0.74, maximum 1.00, mean 0.18, and standard deviation 0.39. The count of numerical predictors with outliers is 18 with the minimum percentage of 0.00%, maximum percentage of 16.97%, average percentage of 1.89%, and standard deviation percentage of 4.05%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=3), 'learning_rate': 0.01, 'n_estimators': 250} |
1030-191-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 1.50 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 26 times representing a percentage of 0.63%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.814. to 4.001 and kurtosis values of 0.33 to 34.96. 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 -0.11, maximum 1.00, mean 0.65, and standard deviation 0.51. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 4.80%, average percentage of 0.97%, and standard deviation percentage of 2.14%.
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'} |
1016-10-2-2-classification.csv | A multivariate classification time-series dataset consists of 6408 samples and 6 features with 5 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 1.00 showing a Balanced dataset. Among the 6408 samples the target ground-truth class has changed 318 times representing a percentage of 4.99%. 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. 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 156 The numerical predictors also exhibit skewness values ranging from 0.117. to 0.792 and kurtosis values of 0.07 to 0.57. The fractal dimension analysis yields values ranging from -0.43 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 -0.20, maximum 1.00, mean 0.42, and standard deviation 0.50. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 1.76%, average percentage of 0.46%, and standard deviation percentage of 0.74%.
Among the categorical predictors, the count of symbols ranges from 53 to 53 with a minimum entropy value 1.2623368034877198, maximum entropy 1.2623368034877198, mean 1.2623368034877198, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 200.0, 'l1_ratio': 0.00039999999999999996, 'penalty': 'elasticnet', 'solver': 'saga'} |
3001-77.csv | A multivariate classification time-series dataset consists of 168 samples and 1 features with 1 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00
The target column has 3 classes with entropy value 1.27 showing a Unbalanced dataset. Among the 168 samples the target ground-truth class has changed 45 times representing a percentage of 29.22%. There are 1 features in the dataset
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 1 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 1 seasonality components detected in the numerical predictors. The top 1 common seasonality components are represented using sinusoidal waves. of periods 16 The numerical predictors also exhibit skewness values ranging from 0.890. to 0.890 and kurtosis values of 0.76 to 0.76. 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. | SVC | {'C': 1, 'kernel': 'linear'} |
1031-59-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 6091 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 6091 samples the target ground-truth class has changed 1199 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. 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 0.070. to 13.382 and kurtosis values of 0.01 to 230.77. The fractal dimension analysis yields values ranging from -0.76 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 0.13, and standard deviation 0.51. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.43%, maximum percentage of 23.30%, average percentage of 13.82%, and standard deviation percentage of 7.07%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-59-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 6973 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 6973 samples the target ground-truth class has changed 1216 times representing a percentage of 17.52%. 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 0.033. to 16.271 and kurtosis values of 0.12 to 333.79. The fractal dimension analysis yields values ranging from -0.72 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.13, and standard deviation 0.52. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.67%, maximum percentage of 38.19%, average percentage of 31.42%, and standard deviation percentage of 9.51%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-15-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 7208 samples and 16 features with 9 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 4280 to 4280 with mean 4280.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.05 showing a Unbalanced dataset. Among the 7208 samples the target ground-truth class has changed 319 times representing a percentage of 11.02%. There are 16 features in the dataset with a ratio of numerical to categorical features of 1.2857142857142858.
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 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 0.126. to 10.443 and kurtosis values of 0.06 to 134.73. The fractal dimension analysis yields values ranging from -0.77 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 -0.32, maximum 1.00, mean 0.24, and standard deviation 0.47. The count of numerical predictors with outliers is 7 with the minimum percentage of 0.00%, maximum percentage of 12.47%, average percentage of 2.10%, and standard deviation percentage of 4.02%.
Among the categorical predictors, the count of symbols ranges from 59 to 71 with a minimum entropy value 4.351387407279858, maximum entropy 5.020963381285738, mean 4.840484757251818, and standard deviation 0.2261121643918046,
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} |
1031-5-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7553 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 7553 samples the target ground-truth class has changed 1203 times representing a percentage of 16.00%. There are 16 features in the dataset with a ratio of numerical to categorical features of 15.0.
Among the numerical predictors, the series has 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 0.007. to 1.291 and kurtosis values of 0.03 to 2.28. 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 -0.90, maximum 1.00, mean 0.09, and standard deviation 0.57. The count of numerical predictors with outliers is 15 with the minimum percentage of 6.38%, maximum percentage of 24.23%, average percentage of 17.36%, and standard deviation percentage of 4.87%.
Among the categorical predictors, the count of symbols ranges from 103 to 103 with a minimum entropy value 0.4519444216455838, maximum entropy 0.4519444216455838, mean 0.4519444216455838, 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} |
1030-118-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 1.37 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 68 times representing a percentage of 1.65%. 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 0.144. to 2.666 and kurtosis values of 0.06 to 14.87. The fractal dimension analysis yields values ranging from -0.60 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 -0.29, maximum 1.00, mean 0.59, and standard deviation 0.59. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 4.32%, average percentage of 1.93%, and standard deviation percentage of 1.55%.
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'} |
1016-16-2-5-classification.csv | A multivariate classification time-series dataset consists of 7859 samples and 3 features with 3 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 0.99 showing a Balanced dataset. Among the 7859 samples the target ground-truth class has changed 347 times representing a percentage of 4.43%. There are 3 features in the dataset
Among the numerical predictors, the series has 3 numerical features detected as Stationary out of the 3 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 5 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 163,245,604 The numerical predictors also exhibit skewness values ranging from 0.095. to 0.481 and kurtosis values of 0.63 to 0.86. The fractal dimension analysis yields values ranging from -0.41 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.01, maximum 1.00, mean 0.54, and standard deviation 0.50. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 1.06%, average percentage of 0.35%, and standard deviation percentage of 0.61%.
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': 200} |
1016-9-2-3-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 361 times representing a percentage of 5.10%. 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 7 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 209,646,1421 The numerical predictors also exhibit skewness values ranging from 0.192. to 0.993 and kurtosis values of 0.03 to 0.95. The fractal dimension analysis yields values ranging from -0.50 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.27, maximum 1.00, mean 0.39, and standard deviation 0.54. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 0.57%, average percentage of 0.19%, and standard deviation percentage of 0.24%.
Among the categorical predictors, the count of symbols ranges from 9 to 65 with a minimum entropy value 1.0752529896413296, maximum entropy 5.254726756638846, mean 3.542336346420772, and standard deviation 1.5443958754112406,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 181.8181818181818, 'l1_ratio': 0.00055, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-46-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7382 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 7382 samples the target ground-truth class has changed 1088 times representing a percentage of 14.81%. There are 16 features in the dataset
Among the numerical predictors, the series has 14 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.025. to 20.235 and kurtosis values of 0.56 to 532.95. 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 -0.89, maximum 1.00, mean 0.10, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.04%, maximum percentage of 49.56%, average percentage of 36.77%, and standard deviation percentage of 15.09%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-54-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 6629 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 6629 samples the target ground-truth class has changed 1254 times representing a percentage of 19.01%. 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 0.001. to 2.091 and kurtosis values of 0.27 to 6.42. The fractal dimension analysis yields values ranging from -0.57 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 0.12, and standard deviation 0.53. The count of numerical predictors with outliers is 14 with the minimum percentage of 0.00%, maximum percentage of 18.29%, average percentage of 13.07%, and standard deviation percentage of 5.44%.
Among the categorical predictors, the count of symbols ranges from 106 to 106 with a minimum entropy value 0.4397445671155972, maximum entropy 0.4397445671155972, mean 0.4397445671155972, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1031-3-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 5881 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 5881 samples the target ground-truth class has changed 985 times representing a percentage of 16.85%. 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 0.019. to 10.557 and kurtosis values of 0.05 to 329.44. The fractal dimension analysis yields values ranging from -0.70 to -0.14 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.96, maximum 1.00, mean 0.10, and standard deviation 0.47. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.42%, maximum percentage of 37.08%, average percentage of 28.73%, and standard deviation percentage of 9.97%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 5, 'n_estimators': 50} |
1031-39-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 3516 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 1.42 showing a Unbalanced dataset. Among the 3516 samples the target ground-truth class has changed 679 times representing a percentage of 19.50%. 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 12 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.209. to 1.285 and kurtosis values of 0.22 to 4.32. The fractal dimension analysis yields values ranging from -0.60 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 -0.83, 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 0.00%, maximum percentage of 24.01%, average percentage of 10.49%, and standard deviation percentage of 5.46%.
Among the categorical predictors, the count of symbols ranges from 13 to 13 with a minimum entropy value 0.05494668761882462, maximum entropy 0.05494668761882462, mean 0.05494668761882462, 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} |
1016-10-2-5-classification.csv | A multivariate classification time-series dataset consists of 6407 samples and 6 features with 4 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 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 6407 samples the target ground-truth class has changed 283 times representing a percentage of 4.44%. There are 6 features in the dataset with a ratio of numerical to categorical features of 2.0.
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 2 seasonality components detected in the numerical predictors. The top 2 common seasonality components are represented using sinusoidal waves. of periods 81,400 The numerical predictors also exhibit skewness values ranging from 0.075. to 0.551 and kurtosis values of 0.40 to 0.84. The fractal dimension analysis yields values ranging from -0.39 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 -0.16, maximum 1.00, mean 0.56, and standard deviation 0.54. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 2.18%, average percentage of 0.61%, and standard deviation percentage of 1.06%.
Among the categorical predictors, the count of symbols ranges from 41 to 64 with a minimum entropy value 1.0513563218418243, maximum entropy 5.55584520704429, mean 3.303600764443057, and standard deviation 2.252244442601233,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 20, 'n_estimators': 400} |
1031-13-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 6147 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.41 showing a Unbalanced dataset. Among the 6147 samples the target ground-truth class has changed 1277 times representing a percentage of 20.89%. 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 0.109. to 12.398 and kurtosis values of 0.21 to 199.36. 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 -0.93, maximum 1.00, mean 0.07, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.78%, maximum percentage of 15.79%, average percentage of 8.73%, and standard deviation percentage of 3.93%.
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} |
1031-11-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 6663 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 6663 samples the target ground-truth class has changed 1270 times representing a percentage of 19.16%. 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. 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 0.070. to 15.247 and kurtosis values of 0.05 to 301.15. The fractal dimension analysis yields values ranging from -0.63 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.93, 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 1.93%, maximum percentage of 13.86%, average percentage of 8.73%, and standard deviation percentage of 4.04%.
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} |
1030-297-classification.csv | A multivariate classification time-series dataset consists of 4049 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 4049 samples the target ground-truth class has changed 59 times representing a percentage of 1.46%. 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. 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 0.176. to 5.042 and kurtosis values of 0.63 to 59.38. The fractal dimension analysis yields values ranging from -0.62 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 -0.34, maximum 1.00, mean 0.57, and standard deviation 0.58. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 4.91%, average percentage of 0.98%, and standard deviation percentage of 2.20%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 40, 'n_estimators': 250} |
1016-9-2-5-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 388 times representing a percentage of 5.48%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.5.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 6 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 154,187,1421 The numerical predictors also exhibit skewness values ranging from 0.173. to 0.415 and kurtosis values of 0.21 to 0.42. The fractal dimension analysis yields values ranging from -0.45 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.05, maximum 1.00, mean 0.60, and standard deviation 0.49. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.01%, maximum percentage of 1.22%, average percentage of 0.46%, and standard deviation percentage of 0.53%.
Among the categorical predictors, the count of symbols ranges from 9 to 64 with a minimum entropy value 1.015510926208203, maximum entropy 5.339031724122719, mean 3.583261776053285, and standard deviation 1.4628101451708735,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-34-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7356 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 1.40 showing a Unbalanced dataset. Among the 7356 samples the target ground-truth class has changed 1444 times representing a percentage of 19.72%. 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 0.088. to 15.331 and kurtosis values of 0.02 to 340.87. The fractal dimension analysis yields values ranging from -0.72 to -0.14 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.95, maximum 1.00, mean 0.13, and standard deviation 0.53. The count of numerical predictors with outliers is 15 with the minimum percentage of 3.55%, maximum percentage of 21.57%, average percentage of 12.25%, and standard deviation percentage of 6.52%.
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} |
1031-20-2-1-2-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 1.44 showing a Unbalanced dataset. Among the 7619 samples the target ground-truth class has changed 1438 times representing a percentage of 18.96%. 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 0.005. to 15.212 and kurtosis values of 0.07 to 316.23. 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 -0.97, maximum 1.00, mean 0.12, and standard deviation 0.53. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.23%, maximum percentage of 18.88%, average percentage of 5.84%, and standard deviation percentage of 4.97%.
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} |
1031-7-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 6600 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 6600 samples the target ground-truth class has changed 246 times representing a percentage of 3.75%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 1.332. to 36.006 and kurtosis values of 5.34 to 1299.61. The fractal dimension analysis yields values ranging from -0.67 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 0.78, and standard deviation 0.29. The count of numerical predictors with outliers is 16 with the minimum percentage of 17.01%, maximum percentage of 17.01%, average percentage of 17.01%, 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': 3, 'n_estimators': 20, 'reg_lambda': 0.2} |
1015-classification.csv | A multivariate classification time-series dataset consists of 1753 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 1.44 showing a Unbalanced dataset. Among the 1753 samples the target ground-truth class has changed 8 times representing a percentage of 0.46%. 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 0.586. to 2.651 and kurtosis values of 0.79 to 12.14. The fractal dimension analysis yields values ranging from -0.57 to -0.35 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.20, maximum 1.00, mean 0.55, and standard deviation 0.58. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.57%, average percentage of 1.39%, and standard deviation percentage of 2.79%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-32-1-1-2-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 1.48 showing a Unbalanced dataset. Among the 7547 samples the target ground-truth class has changed 1476 times representing a percentage of 19.65%. 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. 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 0.006. to 14.375 and kurtosis values of 0.06 to 257.20. 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 -0.92, maximum 1.00, mean 0.11, and standard deviation 0.52. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.69%, maximum percentage of 13.99%, average percentage of 6.58%, and standard deviation percentage of 4.48%.
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} |
1030-323-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 1.54 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 22 times representing a percentage of 0.53%. 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 1.248. to 6.814 and kurtosis values of 0.70 to 82.18. The fractal dimension analysis yields values ranging from -0.62 to -0.29 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.03, maximum 1.00, mean 0.67, and standard deviation 0.48. The count of numerical predictors with outliers is 5 with the minimum percentage of 2.93%, maximum percentage of 4.83%, average percentage of 3.44%, and standard deviation percentage of 0.79%.
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'} |
1031-13-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7437 samples and 13 features with 13 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.38 showing a Unbalanced dataset. Among the 7437 samples the target ground-truth class has changed 1515 times representing a percentage of 20.46%. There are 13 features in the dataset
Among the numerical predictors, the series has 13 numerical features detected as Stationary out of the 13 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.009. to 15.627 and kurtosis values of 0.22 to 319.85. 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 -0.88, maximum 1.00, mean 0.12, and standard deviation 0.54. The count of numerical predictors with outliers is 13 with the minimum percentage of 3.53%, maximum percentage of 14.59%, average percentage of 9.94%, and standard deviation percentage of 3.62%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 5, 'n_estimators': 50} |
1031-32-2-1-6-classification.csv | A multivariate classification time-series dataset consists of 7324 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 7324 samples the target ground-truth class has changed 1409 times representing a percentage of 19.33%. 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 12 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 0.059. to 3.124 and kurtosis values of 0.12 to 16.32. The fractal dimension analysis yields values ranging from -0.56 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 -0.88, maximum 1.00, mean 0.17, and standard deviation 0.52. The count of numerical predictors with outliers is 14 with the minimum percentage of 0.07%, maximum percentage of 13.79%, average percentage of 6.27%, and standard deviation percentage of 5.11%.
Among the categorical predictors, the count of symbols ranges from 104 to 104 with a minimum entropy value 0.4787955353496527, maximum entropy 0.4787955353496527, mean 0.4787955353496527, 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} |
3001-85.csv | A multivariate classification time-series dataset consists of 384 samples and 3 features with 3 numerical and 0 categorical features. Each instance has a window length of 4. The dataset has a sampling rate of 360.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.47 showing a Unbalanced dataset. Among the 384 samples the target ground-truth class has changed 352 times representing a percentage of 95.14%. There are 3 features in the dataset
Among the numerical predictors, the series has 2 numerical features detected as Stationary out of the 3 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 1 seasonality components detected in the numerical predictors. The top 1 common seasonality components are represented using sinusoidal waves. of periods 27 The numerical predictors also exhibit skewness values ranging from 0.057. to 1.724 and kurtosis values of 0.63 to 3.00. The fractal dimension analysis yields values ranging from -1.89 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 0.43, maximum 1.00, mean 0.64, and standard deviation 0.26. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 5.95%, average percentage of 2.88%, and standard deviation percentage of 2.98%.
The dataset is converted into a simple classification task by extracting the previously described features. | LightgbmClassifier | {'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.001, 'max_depth': 3, 'n_estimators': 30, 'num_leaves': 50, 'reg_lambda ': 0.01} |
1031-59-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 7037 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 7037 samples the target ground-truth class has changed 1497 times representing a percentage of 21.38%. 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 0.043. to 16.923 and kurtosis values of 0.05 to 450.98. The fractal dimension analysis yields values ranging from -0.73 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.13, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.46%, maximum percentage of 19.58%, average percentage of 10.70%, and standard deviation percentage of 6.02%.
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} |
1031-9-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 7900 samples and 15 features with 15 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.43 showing a Unbalanced dataset. Among the 7900 samples the target ground-truth class has changed 1722 times representing a percentage of 21.89%. 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 0.001. to 2.056 and kurtosis values of 0.04 to 8.27. 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 -0.95, maximum 1.00, mean 0.13, and standard deviation 0.53. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.89%, maximum percentage of 18.80%, average percentage of 10.81%, and standard deviation percentage of 6.15%.
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} |
1030-127-classification.csv | A multivariate classification time-series dataset consists of 622 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 622 samples the target ground-truth class has changed 14 times representing a percentage of 2.28%. 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 0.182. to 5.883 and kurtosis values of 0.80 to 68.18. 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 -0.31, maximum 1.00, mean 0.58, and standard deviation 0.60. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 8.16%, average percentage of 1.63%, and standard deviation percentage of 3.65%.
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'} |
1030-261-classification.csv | A multivariate classification time-series dataset consists of 2072 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 2072 samples the target ground-truth class has changed 16 times representing a percentage of 0.78%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.628. to 14.381 and kurtosis values of 0.85 to 331.43. The fractal dimension analysis yields values ranging from -0.63 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 0.19, maximum 1.00, mean 0.74, and standard deviation 0.38. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.06%, average percentage of 1.01%, and standard deviation percentage of 2.26%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 100} |
3001-40.csv | A multivariate classification time-series dataset consists of 168 samples and 2 features with 2 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 168 samples the target ground-truth class has changed 1 times representing a percentage of 0.63%. There are 2 features in the dataset
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 2 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.109. to 2.181 and kurtosis values of 1.94 to 9.13. The fractal dimension analysis yields values ranging from -0.89 to -0.62 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.46, maximum 1.00, mean 0.73, and standard deviation 0.27. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 3.16%, average percentage of 1.58%, and standard deviation percentage of 2.24%.
The dataset is converted into a simple classification task by extracting the previously described features. | LightgbmClassifier | {'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 30, 'num_leaves': 50, 'reg_lambda ': 0.01} |
1030-380-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 45 times representing a percentage of 1.09%. 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. 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 0.071. to 2.182 and kurtosis values of 1.07 to 8.06. The fractal dimension analysis yields values ranging from -0.61 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 -0.55, maximum 1.00, mean 0.50, and standard deviation 0.71. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.58%, average percentage of 1.12%, and standard deviation percentage of 2.49%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-55-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7089 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 7089 samples the target ground-truth class has changed 1264 times representing a percentage of 17.92%. 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 0.002. to 1.766 and kurtosis values of 0.01 to 4.16. 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 -0.90, maximum 1.00, mean 0.12, and standard deviation 0.53. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.45%, maximum percentage of 13.25%, average percentage of 7.79%, and standard deviation percentage of 4.34%.
Among the categorical predictors, the count of symbols ranges from 98 to 98 with a minimum entropy value 0.39210776212970316, maximum entropy 0.39210776212970316, mean 0.39210776212970316, 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} |
1031-51-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 6709 samples and 15 features with 15 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.36 showing a Unbalanced dataset. Among the 6709 samples the target ground-truth class has changed 1081 times representing a percentage of 16.19%. There are 15 features in the dataset
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.125. to 3.420 and kurtosis values of 0.22 to 22.44. 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 -0.97, maximum 1.00, mean 0.10, and standard deviation 0.52. The count of numerical predictors with outliers is 15 with the minimum percentage of 14.31%, maximum percentage of 41.03%, average percentage of 31.86%, and standard deviation percentage of 7.53%.
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'} |
1031-14-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7434 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 1.37 showing a Unbalanced dataset. Among the 7434 samples the target ground-truth class has changed 1372 times representing a percentage of 18.54%. 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. 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 2.510 and kurtosis values of 0.08 to 9.39. The fractal dimension analysis yields values ranging from -0.64 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 -0.76, maximum 1.00, mean 0.17, and standard deviation 0.48. The count of numerical predictors with outliers is 15 with the minimum percentage of 9.39%, maximum percentage of 28.27%, average percentage of 19.92%, and standard deviation percentage of 6.70%.
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': 10, 'reg_lambda': 0.2} |
1031-44-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7996 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 1.37 showing a Unbalanced dataset. Among the 7996 samples the target ground-truth class has changed 1106 times representing a percentage of 13.89%. 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 0.050. to 2.034 and kurtosis values of 1.45 to 14.59. The fractal dimension analysis yields values ranging from -0.55 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 0.12, and standard deviation 0.52. The count of numerical predictors with outliers is 15 with the minimum percentage of 37.93%, maximum percentage of 46.91%, average percentage of 44.67%, and standard deviation percentage of 3.08%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1030-144-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 31 times representing a percentage of 0.75%. 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 0.188. to 5.598 and kurtosis values of 1.42 to 53.73. The fractal dimension analysis yields values ranging from -0.65 to -0.33 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.26, 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 6.55%, average percentage of 1.31%, and standard deviation percentage of 2.93%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 20, 'n_estimators': 100} |
1030-298-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 1.57 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 48 times representing a percentage of 1.16%. 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 0.228. to 8.754 and kurtosis values of 1.05 to 177.22. The fractal dimension analysis yields values ranging from -0.61 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 -0.17, maximum 1.00, mean 0.63, and standard deviation 0.54. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.68%, average percentage of 1.14%, and standard deviation percentage of 2.54%.
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'} |
3001-14.csv | A multivariate classification time-series dataset consists of 576 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 1 to 1 with mean 1.00
The target column has 4 classes with entropy value 1.27 showing a Unbalanced dataset. Among the 576 samples the target ground-truth class has changed 377 times representing a percentage of 66.84%. 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 0.253. to 0.253 and kurtosis values of 0.08 to 0.08. The fractal dimension analysis yields values ranging from -1.24 to -1.24 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 1.00, maximum 1.00, mean 1.00, and standard deviation 0.00. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.71%, maximum percentage of 0.71%, average percentage of 0.71%,
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} |
1016-4-2-5-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 360 times representing a percentage of 5.09%. 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 9 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 323,789,1015 The numerical predictors also exhibit skewness values ranging from 0.203. to 0.404 and kurtosis values of 0.30 to 0.39. The fractal dimension analysis yields values ranging from -0.52 to -0.07 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.03, maximum 1.00, mean 0.61, and standard deviation 0.48. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.14%, maximum percentage of 1.96%, average percentage of 0.67%, and standard deviation percentage of 0.87%.
Among the categorical predictors, the count of symbols ranges from 9 to 56 with a minimum entropy value 1.5310103537336097, maximum entropy 5.197724582847469, mean 3.6587079778110025, and standard deviation 1.4374677707280181,
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} |
1031-29-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 7463 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 7463 samples the target ground-truth class has changed 933 times representing a percentage of 12.56%. 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 0.042. to 1.830 and kurtosis values of 1.38 to 8.87. 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 -0.95, maximum 1.00, mean 0.12, and standard deviation 0.52. The count of numerical predictors with outliers is 15 with the minimum percentage of 23.65%, maximum percentage of 42.86%, average percentage of 41.58%, and standard deviation percentage of 4.96%.
Among the categorical predictors, the count of symbols ranges from 32 to 32 with a minimum entropy value 0.3098686015075068, maximum entropy 0.3098686015075068, mean 0.3098686015075068, 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} |
1030-162-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 1.43 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 48 times representing a percentage of 1.16%. 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 0.882. to 2.223 and kurtosis values of 1.37 to 10.32. The fractal dimension analysis yields values ranging from -0.60 to -0.34 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.40, maximum 1.00, mean 0.55, and standard deviation 0.65. The count of numerical predictors with outliers is 5 with the minimum percentage of 2.74%, maximum percentage of 4.75%, average percentage of 3.76%, and standard deviation percentage of 0.72%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-17-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 7409 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 7409 samples the target ground-truth class has changed 1491 times representing a percentage of 20.22%. 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. 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 0.132. to 11.571 and kurtosis values of 0.12 to 176.54. The fractal dimension analysis yields values ranging from -0.63 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.73, maximum 1.00, mean 0.10, and standard deviation 0.50. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 11.57%, average percentage of 6.63%, and standard deviation percentage of 3.63%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-81-1-1-classification.csv | A multivariate classification time-series dataset consists of 7800 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 7800 samples the target ground-truth class has changed 1466 times representing a percentage of 18.88%. 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 0.031. to 14.354 and kurtosis values of 0.01 to 260.34. 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 -0.93, 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 0.50%, maximum percentage of 18.76%, average percentage of 7.17%, and standard deviation percentage of 5.06%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 40, 'n_estimators': 250} |
1031-25-2-1-6-classification.csv | A multivariate classification time-series dataset consists of 7380 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 7380 samples the target ground-truth class has changed 1514 times representing a percentage of 20.61%. 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 0.055. to 16.169 and kurtosis values of 0.29 to 329.98. 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 -0.68, maximum 1.00, mean 0.16, and standard deviation 0.46. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.71%, maximum percentage of 25.91%, average percentage of 15.20%, and standard deviation percentage of 6.75%.
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} |
1020-37-3-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 1.41 showing a Unbalanced dataset. Among the 7012 samples the target ground-truth class has changed 566 times representing a percentage of 8.11%. 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 37 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 1001,1168,1402 The numerical predictors also exhibit skewness values ranging from 0.291. to 4.025 and kurtosis values of 0.39 to 26.31. The fractal dimension analysis yields values ranging from -0.72 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 -0.79, maximum 1.00, mean 0.16, and standard deviation 0.46. The count of numerical predictors with outliers is 7 with the minimum percentage of 0.00%, maximum percentage of 11.61%, average percentage of 4.01%, and standard deviation percentage of 3.79%.
Among the categorical predictors, the count of symbols ranges from 17 to 72 with a minimum entropy value 0.6152757559551011, maximum entropy 3.9583118331751344, mean 2.2867937945651176, and standard deviation 1.6715180386100166,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1030-112-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 23 times representing a percentage of 0.56%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.598. to 6.891 and kurtosis values of 0.83 to 110.19. The fractal dimension analysis yields values ranging from -0.63 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 -0.44, maximum 1.00, mean 0.54, and standard deviation 0.67. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 4.78%, average percentage of 0.96%, and standard deviation percentage of 2.14%.
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'} |
1031-24-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7630 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 1.44 showing a Unbalanced dataset. Among the 7630 samples the target ground-truth class has changed 1136 times representing a percentage of 14.96%. 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 0.098. to 21.070 and kurtosis values of 1.90 to 442.08. The fractal dimension analysis yields values ranging from -0.62 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.03, maximum 1.00, mean 0.88, and standard deviation 0.31. The count of numerical predictors with outliers is 14 with the minimum percentage of 0.00%, maximum percentage of 6.85%, average percentage of 2.44%, and standard deviation percentage of 2.16%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-14-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 7441 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 1.44 showing a Unbalanced dataset. Among the 7441 samples the target ground-truth class has changed 1512 times representing a percentage of 20.41%. 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. 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 2.170 and kurtosis values of 0.21 to 5.95. The fractal dimension analysis yields values ranging from -0.71 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 -0.78, maximum 1.00, mean 0.19, and standard deviation 0.48. The count of numerical predictors with outliers is 14 with the minimum percentage of 0.00%, maximum percentage of 16.11%, average percentage of 8.35%, and standard deviation percentage of 4.33%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 10, 'reg_lambda': 0.2} |
1030-335-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 13 times representing a percentage of 0.32%. 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 0.990. to 3.836 and kurtosis values of 0.31 to 26.52. The fractal dimension analysis yields values ranging from -0.57 to -0.29 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.46, maximum 1.00, mean 0.53, and standard deviation 0.68. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.65%, average percentage of 1.13%, and standard deviation percentage of 2.53%.
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'} |
3001-53.csv | A multivariate classification time-series dataset consists of 504 samples and 1 features with 1 numerical and 0 categorical features. Each instance has a window length of 3. The dataset has a sampling rate of 480.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 504 samples the target ground-truth class has changed 1 times representing a percentage of 0.20%. 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 0.873. to 0.873 and kurtosis values of 0.55 to 0.55. The fractal dimension analysis yields values ranging from -0.49 to -0.49 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': 150} |
1016-25-6-1-classification.csv | A multivariate classification time-series dataset consists of 7110 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 37 to 37 with mean 37.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7110 samples the target ground-truth class has changed 352 times representing a percentage of 5.00%. 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 11 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 150,261,1010 The numerical predictors also exhibit skewness values ranging from 0.038. to 0.650 and kurtosis values of 0.29 to 0.64. The fractal dimension analysis yields values ranging from -0.46 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.02, maximum 1.00, mean 0.63, and standard deviation 0.45. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 2.63%, average percentage of 0.86%, and standard deviation percentage of 1.19%.
Among the categorical predictors, the count of symbols ranges from 51 to 71 with a minimum entropy value 2.0831617863550194, maximum entropy 5.297266045705353, mean 4.234687630763655, and standard deviation 1.2704895434274788,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1029-28-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 4 classes with entropy value 1.92 showing a Unbalanced dataset. Among the 3503 samples the target ground-truth class has changed 95 times representing a percentage of 2.73%. 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 0.526. to 3.613 and kurtosis values of 0.01 to 29.93. The fractal dimension analysis yields values ranging from -0.64 to -0.31 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.46, maximum 1.00, mean 0.45, and standard deviation 0.70. The count of numerical predictors with outliers is 4 with the minimum percentage of 1.69%, maximum percentage of 5.02%, average percentage of 2.60%, and standard deviation percentage of 1.62%.
The dataset is converted into a simple classification task by extracting the previously described features. | LightgbmClassifier | {'boosting_type': 'gbdt', 'colsample_bytree': 0.8, 'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 50, 'num_leaves': 50, 'reg_lambda ': 0.01} |
1031-30-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 7307 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 7307 samples the target ground-truth class has changed 306 times representing a percentage of 4.21%. 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 0.009. to 16.193 and kurtosis values of 7.13 to 355.50. 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 -0.98, 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 18.37%, maximum percentage of 18.37%, average percentage of 18.37%, and standard deviation percentage of 0.00%.
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'} |
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