dataset_name stringlengths 8 32 | series_description stringlengths 1.32k 2.25k | algorithm stringclasses 8 values | hyperparameters stringclasses 93 values |
|---|---|---|---|
1030-263-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 4140 samples the target ground-truth class has changed 73 times representing a percentage of 1.77%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.045. to 4.631 and kurtosis values of 0.56 to 36.60. 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 -0.14, maximum 1.00, mean 0.60, and standard deviation 0.48. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.75%, average percentage of 1.15%, and standard deviation percentage of 2.57%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1016-5-3-5-classification.csv | A multivariate classification time-series dataset consists of 7108 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.52 showing a Unbalanced dataset. Among the 7108 samples the target ground-truth class has changed 597 times representing a percentage of 8.44%. 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 19 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 507,546,592 The numerical predictors also exhibit skewness values ranging from 0.163. to 0.683 and kurtosis values of 0.23 to 0.42. 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 0.03, maximum 1.00, mean 0.62, and standard deviation 0.45. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.23%, maximum percentage of 2.29%, average percentage of 0.83%, and standard deviation percentage of 0.98%.
Among the categorical predictors, the count of symbols ranges from 44 to 60 with a minimum entropy value 1.6328104341304133, maximum entropy 5.214669061675053, mean 3.971916573904216, and standard deviation 1.409400683572674,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 100.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-34-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7667 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 7667 samples the target ground-truth class has changed 1566 times representing a percentage of 20.52%. 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. 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 0.030. to 16.326 and kurtosis values of 0.10 to 350.83. The fractal dimension analysis yields values ranging from -0.69 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 -0.97, maximum 1.00, mean 0.10, and standard deviation 0.52. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.96%, maximum percentage of 18.14%, average percentage of 7.58%, and standard deviation percentage of 5.68%.
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} |
1031-44-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 7996 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 7996 samples the target ground-truth class has changed 1284 times representing a percentage of 16.13%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.020. to 19.426 and kurtosis values of 0.68 to 542.74. The fractal dimension analysis yields values ranging from -0.64 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.96, maximum 1.00, mean 0.13, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.60%, maximum percentage of 37.69%, average percentage of 29.20%, and standard deviation percentage of 9.47%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
3001-97.csv | A multivariate classification time-series dataset consists of 1168 samples and 1 features with 1 numerical and 0 categorical features. Each instance has a window length of 4. The dataset has a sampling rate of 360.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00
The target column has 3 classes with entropy value 1.47 showing a Unbalanced dataset. Among the 1168 samples the target ground-truth class has changed 789 times representing a percentage of 68.13%. There are 1 features in the dataset
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 1 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 1.915. to 1.915 and kurtosis values of 4.54 to 4.54. The fractal dimension analysis yields values ranging from -1.11 to -1.11 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 5.70%, maximum percentage of 5.70%, average percentage of 5.70%,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 100} |
1031-40-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 6861 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 6861 samples the target ground-truth class has changed 1462 times representing a percentage of 21.41%. 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 0.021. to 11.774 and kurtosis values of 0.02 to 211.42. The fractal dimension analysis yields values ranging from -0.70 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.95, maximum 1.00, mean 0.08, and standard deviation 0.51. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.82%, maximum percentage of 11.84%, average percentage of 7.35%, and standard deviation percentage of 2.54%.
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} |
1031-17-1-1-6-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 1437 times representing a percentage of 19.48%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.037. to 14.048 and kurtosis values of 0.04 to 269.09. 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 -0.88, maximum 1.00, mean 0.09, and standard deviation 0.51. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 12.39%, average percentage of 6.54%, and standard deviation percentage of 3.51%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1020-32-3-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 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7012 samples the target ground-truth class has changed 666 times representing a percentage of 9.54%. 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 33 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 584,1402,2337 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 0.13, and standard deviation 0.50. The count of numerical predictors with outliers is 7 with the minimum percentage of 0.00%, maximum percentage of 10.76%, average percentage of 3.66%, and standard deviation percentage of 3.87%.
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': 30} |
1020-1-4-classification.csv | A multivariate classification time-series dataset consists of 7012 samples and 11 features with 9 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.43 showing a Unbalanced dataset. Among the 7012 samples the target ground-truth class has changed 666 times representing a percentage of 9.54%. 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 50 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 779,1168,1402 The numerical predictors also exhibit skewness values ranging from 0.369. to 3.030 and kurtosis values of 0.43 to 11.49. The fractal dimension analysis yields values ranging from -0.68 to -0.14 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.79, maximum 1.00, mean 0.11, and standard deviation 0.51. The count of numerical predictors with outliers is 7 with the minimum percentage of 0.00%, maximum percentage of 9.56%, average percentage of 3.60%, and standard deviation percentage of 3.73%.
Among the categorical predictors, the count of symbols ranges from 17 to 67 with a minimum entropy value 0.4717944249220278, maximum entropy 3.9242187552322045, mean 2.198006590077116, and standard deviation 1.7262121651550884,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-15-2-1-6-classification.csv | A multivariate classification time-series dataset consists of 7208 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 7208 samples the target ground-truth class has changed 643 times representing a percentage of 8.96%. 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 0.010. to 2.434 and kurtosis values of 0.59 to 11.52. The fractal dimension analysis yields values ranging from -0.72 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 -0.92, maximum 1.00, mean 0.10, and standard deviation 0.54. The count of numerical predictors with outliers is 15 with the minimum percentage of 10.06%, maximum percentage of 47.37%, average percentage of 29.59%, and standard deviation percentage of 12.88%.
Among the categorical predictors, the count of symbols ranges from 56 to 56 with a minimum entropy value 0.30925660127447147, maximum entropy 0.30925660127447147, mean 0.30925660127447147, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1016-2-2-4-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 27 to 27 with mean 27.0 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 206 times representing a percentage of 2.81%. 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 0.220. to 0.401 and kurtosis values of 0.34 to 0.72. The fractal dimension analysis yields values ranging from -0.44 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.09, maximum 1.00, mean 0.66, and standard deviation 0.42. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 0.46%, average percentage of 0.14%, and standard deviation percentage of 0.22%.
Among the categorical predictors, the count of symbols ranges from 42 to 69 with a minimum entropy value 1.284009799834391, maximum entropy 5.328229573680992, mean 4.083882015617764, and standard deviation 1.6284644602773584,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-58-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 6768 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.45 showing a Unbalanced dataset. Among the 6768 samples the target ground-truth class has changed 1369 times representing a percentage of 20.33%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.002. to 13.174 and kurtosis values of 0.02 to 222.83. 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 -0.96, maximum 1.00, mean 0.09, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.69%, maximum percentage of 11.91%, average percentage of 6.16%, and standard deviation percentage of 2.96%.
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} |
1031-40-2-1-6-classification.csv | A multivariate classification time-series dataset consists of 6656 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 6656 samples the target ground-truth class has changed 1343 times representing a percentage of 20.28%. 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 0.079. to 12.683 and kurtosis values of 0.20 to 232.45. The fractal dimension analysis yields values ranging from -0.69 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.96, maximum 1.00, mean 0.11, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.37%, maximum percentage of 11.10%, average percentage of 6.48%, and standard deviation percentage of 2.56%.
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 0.5, 'penalty': 'l1', 'solver': 'saga'} |
1020-35-2-classification.csv | A multivariate classification time-series dataset consists of 7010 samples and 11 features with 9 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 7010 samples the target ground-truth class has changed 792 times representing a percentage of 11.35%. There are 11 features in the dataset with a ratio of numerical to categorical features of 4.5.
Among the numerical predictors, the series has 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 38 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 438,467,876 The numerical predictors also exhibit skewness values ranging from 0.311. to 8.743 and kurtosis values of 0.31 to 183.29. The fractal dimension analysis yields values ranging from -0.74 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.82, maximum 1.00, mean 0.13, and standard deviation 0.48. The count of numerical predictors with outliers is 7 with the minimum percentage of 0.00%, maximum percentage of 8.62%, average percentage of 2.54%, and standard deviation percentage of 2.90%.
Among the categorical predictors, the count of symbols ranges from 17 to 50 with a minimum entropy value 0.3055667798210689, maximum entropy 3.9916754246870734, mean 2.1486211022540713, and standard deviation 1.8430543224330023,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1016-4-1-5-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 0.99 showing a Balanced dataset. Among the 7109 samples the target ground-truth class has changed 315 times representing a percentage of 4.45%. 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 5 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 97,1015,1184 The numerical predictors also exhibit skewness values ranging from 0.047. to 0.535 and kurtosis values of 0.25 to 0.83. The fractal dimension analysis yields values ranging from -0.50 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 0.11, maximum 1.00, mean 0.66, and standard deviation 0.42. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.03%, maximum percentage of 1.47%, average percentage of 0.53%, and standard deviation percentage of 0.64%.
Among the categorical predictors, the count of symbols ranges from 9 to 62 with a minimum entropy value 1.5648290810540126, maximum entropy 5.200743980249239, mean 3.7074777233239438, and standard deviation 1.4366999447496311,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-15-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7205 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 7205 samples the target ground-truth class has changed 1539 times representing a percentage of 21.46%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.032. to 13.432 and kurtosis values of 0.01 to 220.45. 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 0.09, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.24%, maximum percentage of 13.74%, average percentage of 7.14%, and standard deviation percentage of 4.34%.
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} |
1031-58-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 6727 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.48 showing a Unbalanced dataset. Among the 6727 samples the target ground-truth class has changed 1439 times representing a percentage of 21.50%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.002. to 11.230 and kurtosis values of 0.18 to 163.75. 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 0.10, and standard deviation 0.48. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 8.34%, average percentage of 5.22%, and standard deviation percentage of 2.60%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1016-4-3-2-classification.csv | A multivariate classification time-series dataset consists of 7109 samples and 12 features with 4 numerical and 8 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7109 samples the target ground-truth class has changed 322 times representing a percentage of 4.55%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.5.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 3 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 129,151,165 The numerical predictors also exhibit skewness values ranging from 0.093. to 0.624 and kurtosis values of 0.04 to 0.60. The fractal dimension analysis yields values ranging from -0.51 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 -0.04, maximum 1.00, mean 0.61, and standard deviation 0.49. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 0.72%, average percentage of 0.44%, and standard deviation percentage of 0.31%.
Among the categorical predictors, the count of symbols ranges from 9 to 57 with a minimum entropy value 1.314812045592728, maximum entropy 5.097310123316942, mean 3.6453917406981855, and standard deviation 1.4257184197075115,
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 10.57371263440564, 'penalty': 'l1', 'solver': 'saga'} |
1031-28-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7627 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 7627 samples the target ground-truth class has changed 1567 times representing a percentage of 20.64%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.039. to 16.788 and kurtosis values of 0.02 to 379.69. The fractal dimension analysis yields values ranging from -0.66 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 -0.82, maximum 1.00, mean 0.22, and standard deviation 0.44. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.50%, maximum percentage of 13.31%, average percentage of 6.77%, and standard deviation percentage of 3.32%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1030-452-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 Balanced dataset. Among the 4140 samples the target ground-truth class has changed 56 times representing a percentage of 1.36%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.207. to 2.073 and kurtosis values of 0.86 to 8.61. The fractal dimension analysis yields values ranging from -0.61 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 -0.19, maximum 1.00, mean 0.62, and standard deviation 0.54. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 3.88%, average percentage of 0.78%, and standard deviation percentage of 1.74%.
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} |
1031-20-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 6366 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 6366 samples the target ground-truth class has changed 1267 times representing a percentage of 20.01%. 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. 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 0.021. to 10.392 and kurtosis values of 0.05 to 134.10. The fractal dimension analysis yields values ranging from -0.65 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.10, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.46%, maximum percentage of 14.77%, average percentage of 6.57%, and standard deviation percentage of 4.92%.
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} |
1020-69-2-classification.csv | A multivariate classification time-series dataset consists of 7010 samples and 11 features with 9 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 7010 samples the target ground-truth class has changed 708 times representing a percentage of 10.15%. 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 34 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 389,412,1402 The numerical predictors also exhibit skewness values ranging from 0.417. to 2.276 and kurtosis values of 0.25 to 7.98. The fractal dimension analysis yields values ranging from -0.75 to -0.15 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.84, maximum 1.00, mean 0.15, and standard deviation 0.51. The count of numerical predictors with outliers is 9 with the minimum percentage of 0.04%, maximum percentage of 5.71%, average percentage of 2.58%, and standard deviation percentage of 2.25%.
Among the categorical predictors, the count of symbols ranges from 17 to 57 with a minimum entropy value 0.35681473151123505, maximum entropy 3.731822762100922, mean 2.0443187468060784, and standard deviation 1.6875040152948435,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-37-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7243 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 7243 samples the target ground-truth class has changed 1579 times representing a percentage of 21.90%. 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 0.033. to 10.609 and kurtosis values of 0.00 to 145.13. The fractal dimension analysis yields values ranging from -0.64 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.10, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.43%, maximum percentage of 19.30%, average percentage of 8.22%, and standard deviation percentage of 4.98%.
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} |
1031-37-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 7822 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 3721 with mean 232.56 and standard deviation 930.25.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 7822 samples the target ground-truth class has changed 789 times representing a percentage of 10.13%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.047. to 15.859 and kurtosis values of 0.63 to 305.85. The fractal dimension analysis yields values ranging from -0.81 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 0.10, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.14%, maximum percentage of 49.24%, average percentage of 27.59%, and standard deviation percentage of 17.75%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-3-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 5588 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.51 showing a Unbalanced dataset. Among the 5588 samples the target ground-truth class has changed 849 times representing a percentage of 15.29%. 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 0.014. to 9.652 and kurtosis values of 0.01 to 124.95. The fractal dimension analysis yields values ranging from -0.72 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.09, and standard deviation 0.49. The count of numerical predictors with outliers is 14 with the minimum percentage of 0.00%, maximum percentage of 22.97%, average percentage of 7.25%, and standard deviation percentage of 6.36%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1028-8-classification.csv | A multivariate classification time-series dataset consists of 6224 samples and 8 features with 8 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 6224 samples the target ground-truth class has changed 57 times representing a percentage of 0.92%. There are 8 features in the dataset
Among the numerical predictors, the series has 8 numerical features detected as Stationary out of the 8 numerical features using the dickey-fuller test and the rest are Unstationary. 8 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 1.557. to 5.549 and kurtosis values of 2.32 to 57.53. The fractal dimension analysis yields values ranging from -0.64 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 -0.11, maximum 1.00, mean 0.68, and standard deviation 0.43. The count of numerical predictors with outliers is 8 with the minimum percentage of 5.78%, maximum percentage of 7.49%, average percentage of 6.22%, and standard deviation percentage of 0.55%.
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'} |
1031-24-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 6690 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 6690 samples the target ground-truth class has changed 1245 times representing a percentage of 18.70%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.059. to 12.153 and kurtosis values of 0.03 to 198.33. 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 -0.96, 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 2.34%, maximum percentage of 16.41%, average percentage of 11.04%, and standard deviation percentage of 4.88%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 5, 'n_estimators': 30} |
1031-54-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 6922 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 6922 samples the target ground-truth class has changed 1163 times representing a percentage of 16.88%. 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. 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 0.013. to 27.841 and kurtosis values of 0.27 to 967.86. The fractal dimension analysis yields values ranging from -0.62 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.95, 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 2.41%, maximum percentage of 42.74%, average percentage of 32.64%, and standard deviation percentage of 10.85%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1016-16-2-1-classification.csv | A multivariate classification time-series dataset consists of 7456 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 1.35 showing a Unbalanced dataset. Among the 7456 samples the target ground-truth class has changed 167 times representing a percentage of 2.25%. 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 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.174. to 3.561 and kurtosis values of 13.14 to 50.68. The fractal dimension analysis yields values ranging from -0.32 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 -0.28, maximum 1.00, mean 0.23, and standard deviation 0.49. The count of numerical predictors with outliers is 5 with the minimum percentage of 10.17%, maximum percentage of 10.17%, average percentage of 10.17%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=1), 'learning_rate': 0.1, 'n_estimators': 50} |
1031-32-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7330 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 7330 samples the target ground-truth class has changed 1226 times representing a percentage of 16.80%. 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. 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 0.007. to 20.556 and kurtosis values of 0.07 to 532.76. The fractal dimension analysis yields values ranging from -0.64 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.91, maximum 1.00, mean 0.13, and standard deviation 0.53. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.62%, maximum percentage of 34.05%, average percentage of 23.57%, and standard deviation percentage of 9.26%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1034-3-1-classification.csv | A multivariate classification time-series dataset consists of 7964 samples and 6 features with 5 numerical and 1 categorical features. Each instance has a window length of 4. The dataset has a sampling rate of 15.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 7964 samples the target ground-truth class has changed 140 times representing a percentage of 1.76%. There are 6 features in the dataset with a ratio of numerical to categorical features of 5.0.
Among the numerical predictors, the series has 5 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.107. to 0.707 and kurtosis values of 0.05 to 0.44. The fractal dimension analysis yields values ranging from -0.76 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 -0.12, maximum 1.00, mean 0.55, and standard deviation 0.37. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.09%, maximum percentage of 2.08%, average percentage of 0.83%, and standard deviation percentage of 0.77%.
Among the categorical predictors, the count of symbols ranges from 69 to 69 with a minimum entropy value 5.469618651722692, maximum entropy 5.469618651722692, mean 5.469618651722692, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-29-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7057 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 7057 samples the target ground-truth class has changed 1356 times representing a percentage of 19.31%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.126. to 24.913 and kurtosis values of 0.05 to 857.87. 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 -0.95, maximum 1.00, mean 0.13, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.90%, maximum percentage of 15.63%, average percentage of 9.66%, and standard deviation percentage of 4.70%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-48-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 7451 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 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 7451 samples the target ground-truth class has changed 1484 times representing a percentage of 20.01%. 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. 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 0.012. to 16.893 and kurtosis values of 0.02 to 427.61. 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 -0.91, maximum 1.00, mean 0.10, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.75%, maximum percentage of 17.76%, average percentage of 7.57%, and standard deviation percentage of 4.84%.
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} |
1030-141-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.36 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 42 times representing a percentage of 1.02%. 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 0.357. to 2.892 and kurtosis values of 0.50 to 14.53. The fractal dimension analysis yields values ranging from -0.62 to -0.31 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.34, maximum 1.00, mean 0.58, and standard deviation 0.62. The count of numerical predictors with outliers is 5 with the minimum percentage of 2.89%, maximum percentage of 5.12%, average percentage of 3.49%, and standard deviation percentage of 0.92%.
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'} |
1031-55-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 7088 samples and 16 features with 15 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7088 samples the target ground-truth class has changed 866 times representing a percentage of 12.28%. 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 0.025. to 1.742 and kurtosis values of 1.34 to 5.89. The fractal dimension analysis yields values ranging from -0.57 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.94, maximum 1.00, mean 0.12, and standard deviation 0.55. The count of numerical predictors with outliers is 15 with the minimum percentage of 29.13%, maximum percentage of 43.34%, average percentage of 40.93%, and standard deviation percentage of 4.46%.
Among the categorical predictors, the count of symbols ranges from 76 to 76 with a minimum entropy value 0.2827563633851321, maximum entropy 0.2827563633851321, mean 0.2827563633851321, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1016-13-3-4-classification.csv | A multivariate classification time-series dataset consists of 6704 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 1.53 showing a Unbalanced dataset. Among the 6704 samples the target ground-truth class has changed 391 times representing a percentage of 5.86%. 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 3 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 24,124,171 The numerical predictors also exhibit skewness values ranging from 0.177. to 0.832 and kurtosis values of 0.31 to 1.48. The fractal dimension analysis yields values ranging from -0.40 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.24, maximum 1.00, mean 0.43, and standard deviation 0.53. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.13%, maximum percentage of 2.49%, average percentage of 0.86%, and standard deviation percentage of 0.98%.
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 14.498501124792053, 'penalty': 'l1', 'solver': 'saga'} |
3001-81.csv | A multivariate classification time-series dataset consists of 288 samples and 1 features with 1 numerical and 0 categorical features. Each instance has a window length of 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 3 classes with entropy value 0.95 showing a Unbalanced dataset. Among the 288 samples the target ground-truth class has changed 185 times representing a percentage of 67.03%. 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 0.32 to 0.32. The fractal dimension analysis yields values ranging from -1.06 to -1.06 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. | ElasticNetClassifier | {'C': 500.0, 'l1_ratio': 0.00019999999999999998, 'penalty': 'elasticnet', 'solver': 'saga'} |
1016-6-4-3-classification.csv | A multivariate classification time-series dataset consists of 6685 samples and 12 features with 10 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 1174 to 1174 with mean 1174.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.47 showing a Unbalanced dataset. Among the 6685 samples the target ground-truth class has changed 659 times representing a percentage of 12.02%. There are 12 features in the dataset with a ratio of numerical to categorical features of 5.0.
Among the numerical predictors, the series has 9 numerical features detected as Stationary out of the 10 numerical features using the dickey-fuller test and the rest are Unstationary. 9 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 23 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 262,275,477 The numerical predictors also exhibit skewness values ranging from 0.071. to 5.477 and kurtosis values of 0.19 to 38.94. The fractal dimension analysis yields values ranging from -0.65 to -0.35 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.68, maximum 1.00, mean 0.16, and standard deviation 0.40. The count of numerical predictors with outliers is 9 with the minimum percentage of 0.00%, maximum percentage of 13.67%, average percentage of 3.90%, and standard deviation percentage of 4.43%.
Among the categorical predictors, the count of symbols ranges from 9 to 66 with a minimum entropy value 2.082376131564408, maximum entropy 2.0994919858523757, mean 2.0909340587083918, and standard deviation 0.008557927143983957,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 0.1, 'n_estimators': 50} |
1031-89-1-1-classification.csv | A multivariate classification time-series dataset consists of 6996 samples and 16 features with 15 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.47 showing a Unbalanced dataset. Among the 6996 samples the target ground-truth class has changed 1452 times representing a percentage of 20.86%. 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 0.071. to 1.029 and kurtosis values of 0.01 to 2.71. The fractal dimension analysis yields values ranging from -0.60 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 0.11, and standard deviation 0.53. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.04%, maximum percentage of 11.13%, average percentage of 5.96%, and standard deviation percentage of 4.13%.
Among the categorical predictors, the count of symbols ranges from 93 to 93 with a minimum entropy value 0.3590468210802921, maximum entropy 0.3590468210802921, mean 0.3590468210802921, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1016-4-3-5-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 326 times representing a percentage of 4.61%. 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 8 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 151,355,592 The numerical predictors also exhibit skewness values ranging from 0.153. to 0.610 and kurtosis values of 0.20 to 0.38. The fractal dimension analysis yields values ranging from -0.51 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 0.08, maximum 1.00, mean 0.65, and standard deviation 0.43. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.20%, maximum percentage of 1.54%, average percentage of 0.62%, and standard deviation percentage of 0.62%.
Among the categorical predictors, the count of symbols ranges from 9 to 56 with a minimum entropy value 1.5301620453475864, maximum entropy 5.051434404958561, mean 3.7052587342961605, and standard deviation 1.3940280698199337,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 100.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-18-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 6862 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 6862 samples the target ground-truth class has changed 917 times representing a percentage of 13.43%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.162. to 16.280 and kurtosis values of 0.53 to 335.26. The fractal dimension analysis yields values ranging from -0.60 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 -0.83, maximum 1.00, mean 0.16, and standard deviation 0.47. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.45%, maximum percentage of 44.49%, average percentage of 31.49%, and standard deviation percentage of 13.67%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.00055, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-23-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 6436 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 1.49 showing a Unbalanced dataset. Among the 6436 samples the target ground-truth class has changed 1360 times representing a percentage of 21.24%. There are 16 features in the dataset with a ratio of numerical to categorical features of 15.0.
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.006. to 1.345 and kurtosis values of 0.13 to 3.31. 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 -0.94, maximum 1.00, mean 0.08, and standard deviation 0.55. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.11%, maximum percentage of 14.26%, average percentage of 6.11%, and standard deviation percentage of 5.04%.
Among the categorical predictors, the count of symbols ranges from 89 to 89 with a minimum entropy value 0.42616374206803753, maximum entropy 0.42616374206803753, mean 0.42616374206803753, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-26-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7594 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 7594 samples the target ground-truth class has changed 887 times representing a percentage of 11.73%. 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. 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 19.541 and kurtosis values of 1.24 to 524.22. The fractal dimension analysis yields values ranging from -0.66 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 -0.93, 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 2.94%, maximum percentage of 43.65%, average percentage of 38.40%, and standard deviation percentage of 12.72%.
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} |
1020-50-2-classification.csv | A multivariate classification time-series dataset consists of 7010 samples and 11 features with 9 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 7010 samples the target ground-truth class has changed 972 times representing a percentage of 13.93%. 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 539,584,2336 The numerical predictors also exhibit skewness values ranging from 0.220. to 3.367 and kurtosis values of 0.30 to 15.54. The fractal dimension analysis yields values ranging from -0.73 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.72, maximum 1.00, mean 0.15, and standard deviation 0.47. The count of numerical predictors with outliers is 9 with the minimum percentage of 0.03%, maximum percentage of 10.58%, average percentage of 3.64%, and standard deviation percentage of 3.54%.
Among the categorical predictors, the count of symbols ranges from 17 to 50 with a minimum entropy value 0.3292816004324517, maximum entropy 3.94678516439027, mean 2.138033382411361, and standard deviation 1.8087517819789092,
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} |
1031-24-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 6690 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 6690 samples the target ground-truth class has changed 1325 times representing a percentage of 19.91%. 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 0.033. to 11.936 and kurtosis values of 0.01 to 182.23. 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 -0.96, 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 2.90%, maximum percentage of 13.30%, average percentage of 7.30%, and standard deviation percentage of 3.18%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1020-65-2-classification.csv | A multivariate classification time-series dataset consists of 7010 samples and 11 features with 9 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.39 showing a Unbalanced dataset. Among the 7010 samples the target ground-truth class has changed 766 times representing a percentage of 10.98%. 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 31 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 539,1168,1752 The numerical predictors also exhibit skewness values ranging from 0.386. to 3.031 and kurtosis values of 0.30 to 12.71. The fractal dimension analysis yields values ranging from -0.71 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.81, maximum 1.00, mean 0.14, and standard deviation 0.48. The count of numerical predictors with outliers is 8 with the minimum percentage of 0.00%, maximum percentage of 9.33%, average percentage of 3.00%, and standard deviation percentage of 3.13%.
Among the categorical predictors, the count of symbols ranges from 17 to 64 with a minimum entropy value 0.3883010064539681, maximum entropy 3.6805641689422757, mean 2.034432587698122, and standard deviation 1.646131581244154,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1031-21-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7508 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 7508 samples the target ground-truth class has changed 1520 times representing a percentage of 20.34%. 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 0.038. to 1.354 and kurtosis values of 0.26 to 6.27. The fractal dimension analysis yields values ranging from -0.55 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.13, and standard deviation 0.50. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.07%, maximum percentage of 16.70%, average percentage of 5.91%, and standard deviation percentage of 5.42%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-32-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7477 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 7477 samples the target ground-truth class has changed 1470 times representing a percentage of 19.75%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.003. to 13.720 and kurtosis values of 0.02 to 221.49. The fractal dimension analysis yields values ranging from -0.65 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.89, maximum 1.00, mean 0.11, and standard deviation 0.52. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.08%, maximum percentage of 6.85%, average percentage of 3.96%, and standard deviation percentage of 2.40%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-56-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 5185 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 5185 samples the target ground-truth class has changed 960 times representing a percentage of 18.64%. 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 2.499 and kurtosis values of 0.05 to 10.18. The fractal dimension analysis yields values ranging from -0.71 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.94, 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 3.36%, maximum percentage of 12.83%, average percentage of 7.42%, and standard deviation percentage of 3.08%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-25-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 7705 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.36 showing a Unbalanced dataset. Among the 7705 samples the target ground-truth class has changed 501 times representing a percentage of 6.53%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.705. to 22.362 and kurtosis values of 1.75 to 501.05. The fractal dimension analysis yields values ranging from -0.45 to -0.05 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.09, maximum 1.00, mean 0.89, and standard deviation 0.28. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.20%, maximum percentage of 25.89%, average percentage of 3.41%, and standard deviation percentage of 8.78%.
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} |
1030-477-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 5 classes with entropy value 2.30 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 36 times representing a percentage of 0.87%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.618. to 24.694 and kurtosis values of 0.58 to 1082.58. The fractal dimension analysis yields values ranging from -0.67 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 -0.21, maximum 1.00, mean 0.62, and standard deviation 0.56. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.02%, average percentage of 1.00%, and standard deviation percentage of 2.25%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 0.1, 'n_estimators': 50} |
3001-45.csv | A multivariate classification time-series dataset consists of 720 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 1.37 showing a Unbalanced dataset. Among the 720 samples the target ground-truth class has changed 486 times representing a percentage of 68.64%. 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 0.782. to 0.782 and kurtosis values of 0.32 to 0.32. The fractal dimension analysis yields values ranging from -1.23 to -1.23 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 1.41%, maximum percentage of 1.41%, average percentage of 1.41%,
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} |
3001-43.csv | A multivariate classification time-series dataset consists of 216 samples and 2 features with 2 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 216 samples the target ground-truth class has changed 2 times representing a percentage of 0.99%. 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 0.207. to 0.768 and kurtosis values of 0.96 to 1.90. The fractal dimension analysis yields values ranging from -0.83 to -0.58 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.63, maximum 1.00, mean 0.81, and standard deviation 0.19. The count of numerical predictors with outliers is 0 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=3), 'learning_rate': 0.01, 'n_estimators': 150} |
1031-13-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7472 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 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 7472 samples the target ground-truth class has changed 1422 times representing a percentage of 19.12%. 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 0.000. to 13.465 and kurtosis values of 0.11 to 237.08. 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 -0.91, maximum 1.00, mean 0.09, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.56%, maximum percentage of 17.14%, average percentage of 11.33%, and standard deviation percentage of 3.91%.
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} |
1031-48-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 7032 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 7032 samples the target ground-truth class has changed 1368 times representing a percentage of 19.55%. 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 0.056. to 14.687 and kurtosis values of 0.02 to 292.50. 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 -0.91, maximum 1.00, mean 0.10, and standard deviation 0.49. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 13.56%, average percentage of 6.32%, and standard deviation percentage of 4.51%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1020-38-1-classification.csv | A multivariate classification time-series dataset consists of 7008 samples and 11 features with 9 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 7008 samples the target ground-truth class has changed 1053 times representing a percentage of 15.10%. 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 36 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 778,876,3504 The numerical predictors also exhibit skewness values ranging from 0.020. to 2.993 and kurtosis values of 0.72 to 14.26. The fractal dimension analysis yields values ranging from -0.75 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.75, maximum 1.00, mean 0.15, and standard deviation 0.45. The count of numerical predictors with outliers is 6 with the minimum percentage of 0.00%, maximum percentage of 8.55%, average percentage of 3.63%, and standard deviation percentage of 3.31%.
Among the categorical predictors, the count of symbols ranges from 17 to 65 with a minimum entropy value 0.4474749682491078, maximum entropy 3.930550116432204, mean 2.189012542340656, and standard deviation 1.7415375740915482,
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} |
1031-34-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 6700 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 6700 samples the target ground-truth class has changed 300 times representing a percentage of 4.50%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.209. to 16.363 and kurtosis values of 6.46 to 354.90. The fractal dimension analysis yields values ranging from -0.60 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 -0.96, maximum 1.00, mean 0.09, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 20.52%, maximum percentage of 20.52%, average percentage of 20.52%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
3001-98.csv | A multivariate classification time-series dataset consists of 876 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 876 samples the target ground-truth class has changed 112 times representing a percentage of 12.98%. 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 0.956. to 0.956 and kurtosis values of 0.53 to 0.53. The fractal dimension analysis yields values ranging from -0.53 to -0.53 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 1.27%, maximum percentage of 1.27%, average percentage of 1.27%,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=5), 'learning_rate': 0.01, 'n_estimators': 50} |
1031-16-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 7459 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.44 showing a Unbalanced dataset. Among the 7459 samples the target ground-truth class has changed 1443 times representing a percentage of 19.43%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.032. to 13.405 and kurtosis values of 0.01 to 217.68. 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 -0.86, 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 0.79%, maximum percentage of 11.62%, average percentage of 7.14%, and standard deviation percentage of 3.91%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-54-2-1-6-classification.csv | A multivariate classification time-series dataset consists of 6921 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 6921 samples the target ground-truth class has changed 859 times representing a percentage of 12.47%. 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. 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 0.096. to 1.999 and kurtosis values of 1.56 to 6.74. The fractal dimension analysis yields values ranging from -0.61 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.92, maximum 1.00, mean 0.10, and standard deviation 0.57. The count of numerical predictors with outliers is 15 with the minimum percentage of 30.10%, maximum percentage of 42.75%, average percentage of 40.32%, and standard deviation percentage of 4.59%.
Among the categorical predictors, the count of symbols ranges from 69 to 69 with a minimum entropy value 0.30633892870837365, maximum entropy 0.30633892870837365, mean 0.30633892870837365, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 100.0, 'l1_ratio': 0.001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-23-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 7568 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 7568 samples the target ground-truth class has changed 474 times representing a percentage of 6.29%. 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 0.110. to 2.715 and kurtosis values of 1.21 to 21.71. The fractal dimension analysis yields values ranging from -0.62 to -0.04 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 0.46. The count of numerical predictors with outliers is 16 with the minimum percentage of 25.91%, maximum percentage of 25.91%, average percentage of 25.91%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 0.1, 'n_estimators': 50} |
1031-52-2-1-4-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 1.45 showing a Unbalanced dataset. Among the 6779 samples the target ground-truth class has changed 1407 times representing a percentage of 20.86%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.001. to 12.931 and kurtosis values of 0.01 to 208.08. 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 -0.88, 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 14.90%, average percentage of 7.18%, and standard deviation percentage of 4.36%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-61-1-5-classification.csv | A multivariate classification time-series dataset consists of 7668 samples and 13 features with 13 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.36 showing a Unbalanced dataset. Among the 7668 samples the target ground-truth class has changed 1459 times representing a percentage of 19.11%. 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 0.005. to 14.677 and kurtosis values of 0.68 to 290.68. The fractal dimension analysis yields values ranging from -0.56 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 -0.90, maximum 1.00, mean 0.18, and standard deviation 0.50. The count of numerical predictors with outliers is 13 with the minimum percentage of 3.22%, maximum percentage of 35.91%, average percentage of 27.58%, and standard deviation percentage of 9.66%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 5, 'n_estimators': 50} |
1031-23-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7488 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 7488 samples the target ground-truth class has changed 195 times representing a percentage of 2.62%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.190. to 19.799 and kurtosis values of 5.44 to 396.57. The fractal dimension analysis yields values ranging from -0.29 to -0.06 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.08, maximum 1.00, mean 0.87, and standard deviation 0.28. The count of numerical predictors with outliers is 16 with the minimum percentage of 12.87%, maximum percentage of 12.87%, average percentage of 12.87%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=1), 'learning_rate': 0.1, 'n_estimators': 50} |
3001-13.csv | A multivariate classification time-series dataset consists of 144 samples and 2 features with 2 numerical and 0 categorical features. Each instance has a window length of 3. The dataset has a sampling rate of 480.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.92 showing a Unbalanced dataset. Among the 144 samples the target ground-truth class has changed 88 times representing a percentage of 66.67%. There are 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 0.068. to 0.659 and kurtosis values of 0.40 to 0.76. The fractal dimension analysis yields values ranging from -1.38 to -1.37 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.44, maximum 1.00, mean 0.28, and standard deviation 0.72. The count of numerical predictors with outliers is 0 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=3), 'learning_rate': 0.5, 'n_estimators': 50} |
3001-68.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 4. The dataset has a sampling rate of 360.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 1 to 1 with mean 1.00
The target column has 3 classes with entropy value 1.50 showing a Unbalanced dataset. Among the 576 samples the target ground-truth class has changed 559 times representing a percentage of 99.47%. 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 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 0.033. to 0.033 and kurtosis values of 0.54 to 0.54. The fractal dimension analysis yields values ranging from -1.21 to -1.21 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 1.00, maximum 1.00, mean 1.00, and standard deviation 0.00. The count of numerical predictors with outliers is 0 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 200} |
1031-35-1-1-4-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 1.45 showing a Unbalanced dataset. Among the 7607 samples the target ground-truth class has changed 1581 times representing a percentage of 20.88%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.026. to 13.319 and kurtosis values of 0.13 to 257.52. 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 -0.97, 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 0.90%, maximum percentage of 14.16%, average percentage of 6.72%, and standard deviation percentage of 3.82%.
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} |
1031-22-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 7616 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 7616 samples the target ground-truth class has changed 519 times representing a percentage of 6.84%. There are 16 features in the dataset
Among the numerical predictors, the series has 2 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 0.395. to 2.238 and kurtosis values of 0.26 to 4.31. The fractal dimension analysis yields values ranging from -0.17 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 0.25, maximum 1.00, mean 0.89, and standard deviation 0.21. The count of numerical predictors with outliers is 16 with the minimum percentage of 8.84%, maximum percentage of 43.15%, average percentage of 13.16%, and standard deviation percentage of 11.71%.
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} |
1016-4-2-2-classification.csv | A multivariate classification time-series dataset consists of 7109 samples and 12 features with 4 numerical and 8 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7109 samples the target ground-truth class has changed 280 times representing a percentage of 3.96%. 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 2 seasonality components detected in the numerical predictors. The top 2 common seasonality components are represented using sinusoidal waves. of periods 109,374 The numerical predictors also exhibit skewness values ranging from 0.229. to 0.423 and kurtosis values of 0.11 to 0.76. The fractal dimension analysis yields values ranging from -0.51 to -0.07 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.01, maximum 1.00, mean 0.63, and standard deviation 0.46. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 0.71%, average percentage of 0.22%, and standard deviation percentage of 0.33%.
Among the categorical predictors, the count of symbols ranges from 9 to 57 with a minimum entropy value 1.4352297639760092, maximum entropy 5.2816792235662895, mean 3.707313192916735, and standard deviation 1.4285948100125525,
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 10.57371263440564, 'penalty': 'l1', 'solver': 'saga'} |
1028-13-classification.csv | A multivariate classification time-series dataset consists of 6231 samples and 8 features with 8 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 6231 samples the target ground-truth class has changed 59 times representing a percentage of 0.95%. There are 8 features in the dataset
Among the numerical predictors, the series has 2 numerical features detected as Stationary out of the 8 numerical features using the dickey-fuller test and the rest are Unstationary. 8 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 1.227. to 1.623 and kurtosis values of 0.19 to 4.98. The fractal dimension analysis yields values ranging from -0.63 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 -0.61, maximum 1.00, mean 0.41, and standard deviation 0.75. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 2.86%, average percentage of 0.57%, and standard deviation percentage of 1.10%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=5), 'learning_rate': 0.5, 'n_estimators': 250} |
1031-95-1-1-classification.csv | A multivariate classification time-series dataset consists of 5840 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 5840 samples the target ground-truth class has changed 1384 times representing a percentage of 23.84%. 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 0.000. to 12.020 and kurtosis values of 0.04 to 196.57. 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 -0.98, maximum 1.00, mean 0.12, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.02%, maximum percentage of 13.99%, average percentage of 5.73%, 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} |
1031-47-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7398 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7398 samples the target ground-truth class has changed 1172 times representing a percentage of 15.92%. 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 16.260 and kurtosis values of 0.41 to 327.08. 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 -0.79, 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 2.55%, maximum percentage of 37.02%, average percentage of 27.20%, and standard deviation percentage of 10.61%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-9-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7515 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.48 showing a Unbalanced dataset. Among the 7515 samples the target ground-truth class has changed 1572 times representing a percentage of 21.01%. 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. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.023. to 22.826 and kurtosis values of 0.17 to 787.97. 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 -0.97, maximum 1.00, mean 0.12, and standard deviation 0.51. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.04%, maximum percentage of 18.37%, average percentage of 6.02%, and standard deviation percentage of 5.72%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1031-7-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 6729 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.36 showing a Unbalanced dataset. Among the 6729 samples the target ground-truth class has changed 888 times representing a percentage of 13.26%. 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 0.255. to 19.964 and kurtosis values of 1.02 to 508.42. 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 -0.92, maximum 1.00, mean 0.11, and standard deviation 0.53. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.40%, maximum percentage of 46.50%, average percentage of 35.17%, and standard deviation percentage of 14.89%.
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'} |
1016-24-5-3-classification.csv | A multivariate classification time-series dataset consists of 7109 samples and 12 features with 5 numerical and 7 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 7109 samples the target ground-truth class has changed 292 times representing a percentage of 4.13%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.7142857142857143.
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. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 4 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 139,236,507 The numerical predictors also exhibit skewness values ranging from 0.140. to 1.531 and kurtosis values of 0.11 to 0.68. The fractal dimension analysis yields values ranging from -0.64 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 -0.26, maximum 1.00, mean 0.41, and standard deviation 0.53. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 18.86%, average percentage of 4.08%, and standard deviation percentage of 8.28%.
Among the categorical predictors, the count of symbols ranges from 9 to 68 with a minimum entropy value 1.5575224314248066, maximum entropy 5.283604478448053, mean 3.441060923358129, and standard deviation 1.4297026655956704,
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} |
2008.csv | A multivariate classification time-series dataset consists of 1168 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 5 classes with entropy value 1.56 showing a Unbalanced dataset. Among the 1168 samples the target ground-truth class has changed 517 times representing a percentage of 44.88%. There are 4 features in the dataset
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 7 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 33,38,68 The numerical predictors also exhibit skewness values ranging from 0.212. to 3.266 and kurtosis values of 0.62 to 13.39. The fractal dimension analysis yields values ranging from -0.92 to -0.57 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.22, maximum 1.00, mean 0.33, and standard deviation 0.51. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 13.80%, average percentage of 3.95%, and standard deviation percentage of 6.64%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=5), 'learning_rate': 0.01, 'n_estimators': 250} |
3001-19.csv | A multivariate classification time-series dataset consists of 876 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 3 classes with entropy value 1.51 showing a Unbalanced dataset. Among the 876 samples the target ground-truth class has changed 397 times representing a percentage of 45.63%. There are 1 features in the dataset
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 1 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 1.881. to 1.881 and kurtosis values of 4.23 to 4.23. The fractal dimension analysis yields values ranging from -1.05 to -1.05 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 1.00, maximum 1.00, mean 1.00, and standard deviation 0.00. The count of numerical predictors with outliers is 1 with the minimum percentage of 6.21%, maximum percentage of 6.21%, average percentage of 6.21%,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=5), 'learning_rate': 1.0, 'n_estimators': 200} |
1031-20-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7611 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 7611 samples the target ground-truth class has changed 1093 times representing a percentage of 14.43%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.037. to 23.045 and kurtosis values of 0.71 to 646.39. 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 -0.97, 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 2.15%, maximum percentage of 49.12%, average percentage of 40.33%, and standard deviation percentage of 14.82%.
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} |
1031-12-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 6454 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 1.45 showing a Unbalanced dataset. Among the 6454 samples the target ground-truth class has changed 1318 times representing a percentage of 20.53%. There are 13 features in the dataset
Among the numerical predictors, the series has 13 numerical features detected as Stationary out of the 13 numerical features using the dickey-fuller test and the rest are Unstationary. 10 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.010. to 15.971 and kurtosis values of 0.07 to 314.60. The fractal dimension analysis yields values ranging from -0.64 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.93, maximum 1.00, mean 0.11, and standard deviation 0.55. The count of numerical predictors with outliers is 13 with the minimum percentage of 1.85%, maximum percentage of 14.42%, average percentage of 6.96%, and standard deviation percentage of 4.40%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1016-22-1-4-classification.csv | A multivariate classification time-series dataset consists of 7109 samples and 8 features with 8 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.43 showing a Unbalanced dataset. Among the 7109 samples the target ground-truth class has changed 805 times representing a percentage of 11.38%. There are 8 features in the dataset
Among the numerical predictors, the series has 8 numerical features detected as Stationary out of the 8 numerical features using the dickey-fuller test and the rest are Unstationary. 8 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 34 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 1421,1777,3554 The numerical predictors also exhibit skewness values ranging from 0.232. to 8.557 and kurtosis values of 0.09 to 100.78. The fractal dimension analysis yields values ranging from -0.58 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 -0.34, maximum 1.00, mean 0.20, and standard deviation 0.44. The count of numerical predictors with outliers is 7 with the minimum percentage of 0.00%, maximum percentage of 7.49%, average percentage of 2.63%, and standard deviation percentage of 2.31%.
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} |
1031-17-1-1-1-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 1.43 showing a Unbalanced dataset. Among the 7380 samples the target ground-truth class has changed 1527 times representing a percentage of 20.79%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 13.753 and kurtosis values of 0.37 to 247.13. The fractal dimension analysis yields values ranging from -0.67 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.87, maximum 1.00, mean 0.11, and standard deviation 0.48. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 11.97%, average percentage of 6.79%, and standard deviation percentage of 3.41%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1020-37-1-classification.csv | A multivariate classification time-series dataset consists of 7008 samples and 11 features with 9 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.45 showing a Unbalanced dataset. Among the 7008 samples the target ground-truth class has changed 704 times representing a percentage of 10.09%. There are 11 features in the dataset with a ratio of numerical to categorical features of 4.5.
Among the numerical predictors, the series has 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 27 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 539,584,700 The numerical predictors also exhibit skewness values ranging from 0.020. to 2.993 and kurtosis values of 0.72 to 14.26. The fractal dimension analysis yields values ranging from -0.75 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.75, maximum 1.00, mean 0.16, and standard deviation 0.45. The count of numerical predictors with outliers is 6 with the minimum percentage of 0.00%, maximum percentage of 8.55%, average percentage of 3.40%, and standard deviation percentage of 3.31%.
Among the categorical predictors, the count of symbols ranges from 17 to 65 with a minimum entropy value 0.4474749682491078, maximum entropy 3.930550116432204, mean 2.189012542340656, and standard deviation 1.7415375740915482,
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'} |
1030-30-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 5 classes with entropy value 2.31 showing a Balanced dataset. Among the 4140 samples the target ground-truth class has changed 30 times representing a percentage of 0.73%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.857. to 4.815 and kurtosis values of 0.37 to 58.36. 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 -0.31, maximum 1.00, mean 0.58, and standard deviation 0.61. The count of numerical predictors with outliers is 5 with the minimum percentage of 1.24%, maximum percentage of 4.27%, average percentage of 2.19%, and standard deviation percentage of 1.23%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 0.1, 'n_estimators': 50} |
1031-30-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7470 samples and 14 features with 12 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 0.97 showing a Unbalanced dataset. Among the 7470 samples the target ground-truth class has changed 653 times representing a percentage of 8.78%. There are 14 features in the dataset with a ratio of numerical to categorical features of 6.0.
Among the numerical predictors, the series has 11 numerical features detected as Stationary out of the 12 numerical features using the dickey-fuller test and the rest are Unstationary. 12 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.126. to 2.614 and kurtosis values of 0.10 to 10.73. The fractal dimension analysis yields values ranging from -0.78 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 -0.95, maximum 1.00, mean 0.11, and standard deviation 0.51. The count of numerical predictors with outliers is 10 with the minimum percentage of 0.00%, maximum percentage of 6.91%, average percentage of 1.84%, and standard deviation percentage of 2.43%.
Among the categorical predictors, the count of symbols ranges from 51 to 73 with a minimum entropy value 3.4703070741798343, maximum entropy 4.4889574512464145, mean 3.9796322627131246, and standard deviation 0.5093251885332901,
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} |
3001-41.csv | A multivariate classification time-series dataset consists of 648 samples and 3 features with 3 numerical and 0 categorical features. Each instance has a window length of 3. The dataset has a sampling rate of 480.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 648 samples the target ground-truth class has changed 2 times representing a percentage of 0.31%. There are 3 features in the dataset
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 3 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.784. to 1.975 and kurtosis values of 0.02 to 3.43. The fractal dimension analysis yields values ranging from -1.32 to -1.15 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.75, maximum 1.00, mean 0.23, and standard deviation 0.70. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 12.13%, average percentage of 4.09%, and standard deviation percentage of 6.96%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=3), 'learning_rate': 0.01, 'n_estimators': 150} |
3001-69.csv | A multivariate classification time-series dataset consists of 576 samples and 2 features with 2 numerical and 0 categorical features. Each instance has a window length of 3. The dataset has a sampling rate of 480.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 4 classes with entropy value 1.27 showing a Unbalanced dataset. Among the 576 samples the target ground-truth class has changed 376 times representing a percentage of 66.79%. 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 0.028. to 1.748 and kurtosis values of 0.75 to 2.21. The fractal dimension analysis yields values ranging from -1.43 to -1.07 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.48, maximum 1.00, mean 0.74, and standard deviation 0.26. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 10.83%, average percentage of 5.42%, and standard deviation percentage of 7.66%.
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} |
1031-25-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7375 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 7375 samples the target ground-truth class has changed 1211 times representing a percentage of 16.50%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.006. to 16.699 and kurtosis values of 0.32 to 365.80. 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 -0.96, maximum 1.00, mean 0.13, and standard deviation 0.47. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.77%, maximum percentage of 24.45%, average percentage of 16.96%, and standard deviation percentage of 6.10%.
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} |
1031-39-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 7594 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 7594 samples the target ground-truth class has changed 1269 times representing a percentage of 16.79%. 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. 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 21.354 and kurtosis values of 0.11 to 595.93. 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 -0.86, maximum 1.00, mean 0.08, and standard deviation 0.54. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.53%, maximum percentage of 33.02%, average percentage of 21.73%, 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} |
1016-11-6-5-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 0.99 showing a Balanced dataset. Among the 7109 samples the target ground-truth class has changed 201 times representing a percentage of 2.84%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.7142857142857143.
Among the numerical predictors, the series has 5 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.039. to 4.971 and kurtosis values of 0.26 to 38.70. The fractal dimension analysis yields values ranging from -0.60 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 -0.34, maximum 1.00, mean 0.34, and standard deviation 0.49. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 1.57%, average percentage of 0.50%, and standard deviation percentage of 0.72%.
Among the categorical predictors, the count of symbols ranges from 9 to 64 with a minimum entropy value 1.6147273247242908, maximum entropy 5.138741538831351, mean 3.4722852258294528, and standard deviation 1.3537446807080729,
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} |
1031-88-1-1-classification.csv | A multivariate classification time-series dataset consists of 7806 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 1.41 showing a Unbalanced dataset. Among the 7806 samples the target ground-truth class has changed 1554 times representing a percentage of 19.99%. 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 0.092. to 0.876 and kurtosis values of 0.02 to 1.81. 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 -0.82, maximum 1.00, mean 0.11, and standard deviation 0.54. The count of numerical predictors with outliers is 15 with the minimum percentage of 1.13%, maximum percentage of 18.50%, average percentage of 10.25%, and standard deviation percentage of 4.40%.
Among the categorical predictors, the count of symbols ranges from 87 to 87 with a minimum entropy value 0.3833378328214823, maximum entropy 0.3833378328214823, mean 0.3833378328214823, 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} |
2007-16.csv | A multivariate classification time-series dataset consists of 1262 samples and 7 features with 6 numerical and 1 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 1 with mean 0.17 and standard deviation 0.41. 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 0.67 showing a Unbalanced dataset. Among the 1262 samples the target ground-truth class has changed 200 times representing a percentage of 16.06%. There are 7 features in the dataset with a ratio of numerical to categorical features of 6.0.
Among the numerical predictors, the series has 5 numerical features detected as Stationary out of the 6 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 4 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 114,180,210 The numerical predictors also exhibit skewness values ranging from 0.085. to 6.767 and kurtosis values of 0.02 to 60.67. The fractal dimension analysis yields values ranging from -0.90 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 -0.39, maximum 1.00, mean 0.17, and standard deviation 0.45. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 19.36%, average percentage of 3.55%, and standard deviation percentage of 7.76%.
Among the categorical predictors, the count of symbols ranges from 3 to 3 with a minimum entropy value 0.6704419138987988, maximum entropy 0.6704419138987988, mean 0.6704419138987988, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 500.0, 'l1_ratio': 0.00019999999999999998, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-51-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 3892 samples and 16 features with 0 numerical and 16 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%. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 3892 samples the target ground-truth class has changed 15 times representing a percentage of 0.39%. There are 16 features in the dataset with a ratio of numerical to categorical features of 0.0.
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 0 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.
Among the categorical predictors, the count of symbols ranges from 3 to 62 with a minimum entropy value 0.00690780458836204, maximum entropy 0.21051356879469785, mean 0.195221748353144, and standard deviation 0.048689462875751516,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=1), 'learning_rate': 0.1, 'n_estimators': 50} |
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