dataset_name stringlengths 8 32 | series_description stringlengths 1.32k 2.25k | algorithm stringclasses 8 values | hyperparameters stringclasses 93 values |
|---|---|---|---|
1016-11-2-4-classification.csv | A multivariate classification time-series dataset consists of 7109 samples and 12 features with 4 numerical and 8 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 7109 samples the target ground-truth class has changed 338 times representing a percentage of 4.78%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.5.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 5 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 87,120,129 The numerical predictors also exhibit skewness values ranging from 0.095. to 0.476 and kurtosis values of 0.02 to 0.47. The fractal dimension analysis yields values ranging from -0.47 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.11, maximum 1.00, mean 0.58, and standard deviation 0.51. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.03%, maximum percentage of 0.88%, average percentage of 0.54%, and standard deviation percentage of 0.38%.
Among the categorical predictors, the count of symbols ranges from 9 to 70 with a minimum entropy value 1.3204624355220689, maximum entropy 5.3041564219969315, mean 3.685746931868875, and standard deviation 1.4732184986717651,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-11-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 6671 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 6671 samples the target ground-truth class has changed 1283 times representing a percentage of 19.33%. There are 16 features in the dataset
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.015. to 17.041 and kurtosis values of 0.08 to 389.59. 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 -0.92, maximum 1.00, mean 0.10, and standard deviation 0.53. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.08%, maximum percentage of 15.91%, average percentage of 10.88%, and standard deviation percentage of 5.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-53-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 2787 samples and 12 features with 0 numerical and 12 categorical features. Each instance has a 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 2787 samples the target ground-truth class has changed 25 times representing a percentage of 0.90%. There are 12 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 29 to 65 with a minimum entropy value 0.2567144223804823, maximum entropy 0.30175341188870386, mean 0.2862376523170694, and standard deviation 0.01487347999257852,
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} |
1030-48-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 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. 2 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.031. to 2.738 and kurtosis values of 0.55 to 13.59. 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 -0.27, maximum 1.00, mean 0.53, and standard deviation 0.51. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.36%, average percentage of 1.07%, and standard deviation percentage of 2.40%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1030-325-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.50 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 34 times representing a percentage of 0.82%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.117. to 4.132 and kurtosis values of 0.59 to 31.58. The fractal dimension analysis yields values ranging from -0.59 to -0.33 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.33, maximum 1.00, mean 0.56, and standard deviation 0.61. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 5.65%, average percentage of 1.22%, and standard deviation percentage of 2.48%.
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} |
1030-427-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 4 classes with entropy value 1.89 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 25 times representing a percentage of 0.61%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.977. to 13.015 and kurtosis values of 0.06 to 264.15. 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 0.13, maximum 1.00, mean 0.72, and standard deviation 0.41. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.32%, maximum percentage of 5.00%, average percentage of 1.27%, and standard deviation percentage of 2.09%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1016-16-1-2-classification.csv | A multivariate classification time-series dataset consists of 7847 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 2 classes with entropy value 0.98 showing a Unbalanced dataset. Among the 7847 samples the target ground-truth class has changed 336 times representing a percentage of 4.30%. 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. 2 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 15 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 230,653,1961 The numerical predictors also exhibit skewness values ranging from 0.059. to 0.863 and kurtosis values of 0.36 to 0.79. The fractal dimension analysis yields values ranging from -0.57 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.34, maximum 1.00, mean 0.24, and standard deviation 0.50. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 2.43%, average percentage of 0.96%, and standard deviation percentage of 1.23%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 100.0, 'l1_ratio': 0.0007, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-54-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 6917 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 6917 samples the target ground-truth class has changed 1198 times representing a percentage of 17.41%. There are 16 features in the dataset with a ratio of numerical to categorical features of 15.0.
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.033. to 1.821 and kurtosis values of 0.27 to 5.53. The fractal dimension analysis yields values ranging from -0.60 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.93, maximum 1.00, mean 0.11, and standard deviation 0.55. The count of numerical predictors with outliers is 15 with the minimum percentage of 14.69%, maximum percentage of 33.81%, average percentage of 27.70%, and standard deviation percentage of 6.11%.
Among the categorical predictors, the count of symbols ranges from 95 to 95 with a minimum entropy value 0.40885032128192855, maximum entropy 0.40885032128192855, mean 0.40885032128192855, 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} |
1034-3-5-classification.csv | A multivariate classification time-series dataset consists of 7963 samples and 6 features with 6 numerical and 0 categorical features. Each instance has a window length of 4. The dataset has a sampling rate of 15.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.98 showing a Balanced dataset. Among the 7963 samples the target ground-truth class has changed 171 times representing a percentage of 2.15%. There are 6 features in the dataset
Among the numerical predictors, the series has 6 numerical features detected as Stationary out of the 6 numerical features using the dickey-fuller test and the rest are Unstationary. 6 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.103. to 1.910 and kurtosis values of 0.01 to 4.27. The fractal dimension analysis yields values ranging from -0.73 to -0.47 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 0.31, and standard deviation 0.47. The count of numerical predictors with outliers is 6 with the minimum percentage of 0.54%, maximum percentage of 11.71%, average percentage of 5.81%, and standard deviation percentage of 4.60%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 10, 'reg_lambda': 0.2} |
1031-54-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 5444 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 5444 samples the target ground-truth class has changed 1176 times representing a percentage of 21.74%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.007. to 17.226 and kurtosis values of 0.03 to 373.78. The fractal dimension analysis yields values ranging from -0.60 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.12, and standard deviation 0.49. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 10.00%, average percentage of 4.74%, and standard deviation percentage of 2.99%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-53-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 5168 samples and 13 features with 12 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.36 showing a Unbalanced dataset. Among the 5168 samples the target ground-truth class has changed 140 times representing a percentage of 2.73%. There are 13 features in the dataset with a ratio of numerical to categorical features of 12.0.
Among the numerical predictors, the series has 12 numerical features detected as Stationary out of the 12 numerical features using the dickey-fuller test and the rest are Unstationary. 9 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.016. to 7.421 and kurtosis values of 21.01 to 82.00. The fractal dimension analysis yields values ranging from -0.27 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.89, maximum 1.00, mean 0.12, and standard deviation 0.59. The count of numerical predictors with outliers is 12 with the minimum percentage of 9.44%, maximum percentage of 9.44%, average percentage of 9.44%, and standard deviation percentage of 0.00%.
Among the categorical predictors, the count of symbols ranges from 65 to 65 with a minimum entropy value 0.6086337754107652, maximum entropy 0.6086337754107652, mean 0.6086337754107652, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=1), 'learning_rate': 0.1, 'n_estimators': 50} |
1031-23-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7394 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 7394 samples the target ground-truth class has changed 379 times representing a percentage of 5.15%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.899. to 1.193 and kurtosis values of 0.68 to 1.27. The fractal dimension analysis yields values ranging from -0.23 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 0.44, maximum 1.00, mean 0.94, and standard deviation 0.15. The count of numerical predictors with outliers is 16 with the minimum percentage of 35.07%, maximum percentage of 35.07%, average percentage of 35.07%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-35-2-1-6-classification.csv | A multivariate classification time-series dataset consists of 5232 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 5232 samples the target ground-truth class has changed 723 times representing a percentage of 13.91%. 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. 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 0.116. to 15.909 and kurtosis values of 0.53 to 342.97. The fractal dimension analysis yields values ranging from -0.64 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.10, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.42%, maximum percentage of 45.04%, average percentage of 39.15%, and standard deviation percentage of 11.28%.
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'} |
1031-95-1-3-classification.csv | A multivariate classification time-series dataset consists of 5843 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 1.50 showing a Unbalanced dataset. Among the 5843 samples the target ground-truth class has changed 1317 times representing a percentage of 22.67%. 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 0.007. to 11.740 and kurtosis values of 0.22 to 182.08. The fractal dimension analysis yields values ranging from -0.74 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.98, maximum 1.00, mean 0.11, and standard deviation 0.48. The count of numerical predictors with outliers is 14 with the minimum percentage of 0.00%, maximum percentage of 15.06%, average percentage of 5.40%, and standard deviation percentage of 5.10%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-102-1-classification.csv | A multivariate classification time-series dataset consists of 1964 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 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 1964 samples the target ground-truth class has changed 182 times representing a percentage of 9.43%. 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 0.060. to 9.711 and kurtosis values of 0.04 to 125.06. The fractal dimension analysis yields values ranging from -0.78 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 0.48. The count of numerical predictors with outliers is 13 with the minimum percentage of 0.00%, maximum percentage of 14.66%, average percentage of 2.24%, and standard deviation percentage of 3.63%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 20, 'n_estimators': 250} |
1016-5-5-1-classification.csv | A multivariate classification time-series dataset consists of 7108 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7108 samples the target ground-truth class has changed 232 times representing a percentage of 3.28%. There are 8 features in the dataset with a ratio of numerical to categorical features of 1.0.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 3 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.064. to 0.931 and kurtosis values of 0.49 to 1.15. The fractal dimension analysis yields values ranging from -0.40 to -0.10 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.13, maximum 1.00, mean 0.57, and standard deviation 0.53. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 2.93%, average percentage of 1.31%, and standard deviation percentage of 1.21%.
Among the categorical predictors, the count of symbols ranges from 32 to 70 with a minimum entropy value 1.4536172546089863, maximum entropy 5.208806561369607, mean 3.895393113442287, and standard deviation 1.4994086539950475,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 20, 'n_estimators': 400} |
1031-27-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 4825 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 1.34 showing a Unbalanced dataset. Among the 4825 samples the target ground-truth class has changed 225 times representing a percentage of 4.70%. 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. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.039. to 23.763 and kurtosis values of 3.88 to 687.99. The fractal dimension analysis yields values ranging from -0.61 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 0.09, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.34%, maximum percentage of 28.93%, average percentage of 27.20%, and standard deviation percentage of 6.90%.
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'} |
1021-4-classification.csv | A multivariate classification time-series dataset consists of 7012 samples and 6 features with 1 numerical and 5 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 Similarly, the missing values percentages for categorical features range from 0 to 10 with mean 6.0 and standard deviation 5.477225575051661.
The target column has 3 classes with entropy value 1.42 showing a Unbalanced dataset. Among the 7012 samples the target ground-truth class has changed 649 times representing a percentage of 9.30%. There are 6 features in the dataset with a ratio of numerical to categorical features of 0.2.
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 1 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 23 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 411,583,1167 The numerical predictors also exhibit skewness values ranging from 5.277. to 5.277 and kurtosis values of 37.16 to 37.16. The fractal dimension analysis yields values ranging from -0.23 to -0.23 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 9.88%, maximum percentage of 9.88%, average percentage of 9.88%,
Among the categorical predictors, the count of symbols ranges from 5 to 69 with a minimum entropy value 0.45059076085007993, maximum entropy 5.534896670990089, mean 3.740801471657136, and standard deviation 2.145824078723643,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-42-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7045 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 7045 samples the target ground-truth class has changed 1716 times representing a percentage of 24.48%. 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 0.165. to 8.536 and kurtosis values of 0.12 to 103.83. The fractal dimension analysis yields values ranging from -0.70 to -0.14 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.94, maximum 1.00, mean 0.14, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.23%, maximum percentage of 13.56%, average percentage of 7.48%, and standard deviation percentage of 3.37%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-38-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 5363 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 5363 samples the target ground-truth class has changed 814 times representing a percentage of 15.27%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.009. to 14.416 and kurtosis values of 0.73 to 254.87. 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 -0.98, maximum 1.00, mean 0.11, and standard deviation 0.51. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.78%, maximum percentage of 49.71%, average percentage of 39.71%, and standard deviation percentage of 12.69%.
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} |
1031-47-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7355 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 1.39 showing a Unbalanced dataset. Among the 7355 samples the target ground-truth class has changed 1356 times representing a percentage of 18.52%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.003. to 12.264 and kurtosis values of 0.09 to 187.78. The fractal dimension analysis yields values ranging from -0.66 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.88, 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 3.35%, maximum percentage of 12.33%, average percentage of 8.09%, and standard deviation percentage of 2.75%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1016-22-1-3-classification.csv | A multivariate classification time-series dataset consists of 7103 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 1.47 showing a Unbalanced dataset. Among the 7103 samples the target ground-truth class has changed 790 times representing a percentage of 11.18%. 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 33 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 338,394,3551 The numerical predictors also exhibit skewness values ranging from 0.126. to 8.466 and kurtosis values of 0.00 to 97.87. The fractal dimension analysis yields values ranging from -0.55 to -0.07 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.30, maximum 1.00, mean 0.19, and standard deviation 0.44. The count of numerical predictors with outliers is 8 with the minimum percentage of 0.54%, maximum percentage of 7.62%, average percentage of 3.03%, and standard deviation percentage of 2.99%.
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 0.5, 'penalty': 'l1', 'solver': 'saga'} |
1031-47-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 7403 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 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7403 samples the target ground-truth class has changed 698 times representing a percentage of 9.47%. 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 14 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.035. to 1.028 and kurtosis values of 0.13 to 1.10. The fractal dimension analysis yields values ranging from -0.65 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.71, maximum 1.00, mean 0.12, and standard deviation 0.54. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.00%, maximum percentage of 2.31%, average percentage of 0.32%, and standard deviation percentage of 0.65%.
Among the categorical predictors, the count of symbols ranges from 62 to 62 with a minimum entropy value 0.49970164523257954, maximum entropy 0.49970164523257954, mean 0.49970164523257954, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-52-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 5668 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 5668 samples the target ground-truth class has changed 1169 times representing a percentage of 20.75%. 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 0.016. to 9.658 and kurtosis values of 0.07 to 120.32. 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 -0.95, maximum 1.00, mean 0.09, and standard deviation 0.48. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 10.92%, average percentage of 6.24%, and standard deviation percentage of 3.05%.
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} |
1031-50-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7035 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.38 showing a Unbalanced dataset. Among the 7035 samples the target ground-truth class has changed 1262 times representing a percentage of 18.03%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.028. to 18.881 and kurtosis values of 0.29 to 508.01. The fractal dimension analysis yields values ranging from -0.64 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.88, 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 2.81%, maximum percentage of 24.48%, average percentage of 15.17%, and standard deviation percentage of 6.70%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-5-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7536 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 1.43 showing a Unbalanced dataset. Among the 7536 samples the target ground-truth class has changed 1291 times representing a percentage of 17.21%. 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 0.109. to 0.796 and kurtosis values of 0.13 to 1.44. The fractal dimension analysis yields values ranging from -0.61 to -0.10 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.86, maximum 1.00, mean 0.09, and standard deviation 0.57. The count of numerical predictors with outliers is 12 with the minimum percentage of 0.00%, maximum percentage of 19.47%, average percentage of 4.83%, and standard deviation percentage of 5.63%.
Among the categorical predictors, the count of symbols ranges from 85 to 85 with a minimum entropy value 0.39679624583361817, maximum entropy 0.39679624583361817, mean 0.39679624583361817, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1016-16-1-5-classification.csv | A multivariate classification time-series dataset consists of 7470 samples and 4 features with 4 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 1.55 showing a Unbalanced dataset. Among the 7470 samples the target ground-truth class has changed 524 times representing a percentage of 7.05%. 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. 2 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 257,324 The numerical predictors also exhibit skewness values ranging from 0.046. to 0.907 and kurtosis values of 0.30 to 0.88. The fractal dimension analysis yields values ranging from -0.46 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.11, maximum 1.00, mean 0.39, and standard deviation 0.48. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 2.25%, average percentage of 0.91%, and standard deviation percentage of 1.10%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-56-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7417 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 7417 samples the target ground-truth class has changed 1557 times representing a percentage of 21.09%. 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 0.007. to 17.882 and kurtosis values of 0.05 to 418.74. The fractal dimension analysis yields values ranging from -0.73 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.93, maximum 1.00, mean 0.11, and standard deviation 0.47. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.66%, maximum percentage of 12.10%, average percentage of 6.58%, and standard deviation percentage of 3.37%.
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} |
1031-17-1-1-2-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 1.37 showing a Unbalanced dataset. Among the 7409 samples the target ground-truth class has changed 1274 times representing a percentage of 17.27%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.347. to 14.262 and kurtosis values of 0.22 to 264.52. 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 -0.75, 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 3.47%, maximum percentage of 25.40%, average percentage of 15.10%, and standard deviation percentage of 5.69%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1016-24-5-4-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 246 times representing a percentage of 3.48%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.7142857142857143.
Among the numerical predictors, the series has 5 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 1 seasonality components detected in the numerical predictors. The top 1 common seasonality components are represented using sinusoidal waves. of periods 96 The numerical predictors also exhibit skewness values ranging from 0.008. to 1.247 and kurtosis values of 0.39 to 0.84. The fractal dimension analysis yields values ranging from -0.65 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 -0.12, maximum 1.00, mean 0.50, and standard deviation 0.46. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 22.88%, average percentage of 4.89%, and standard deviation percentage of 10.08%.
Among the categorical predictors, the count of symbols ranges from 9 to 64 with a minimum entropy value 1.5201132038452545, maximum entropy 5.309629521512153, mean 3.401687875471214, and standard deviation 1.4106322167530227,
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} |
1016-5-3-1-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 14 to 14 with mean 14.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 7108 samples the target ground-truth class has changed 403 times representing a percentage of 5.71%. There are 8 features in the dataset with a ratio of numerical to categorical features of 1.0.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 3 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 8 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 709,710,788 The numerical predictors also exhibit skewness values ranging from 0.099. to 0.420 and kurtosis values of 0.01 to 0.57. The fractal dimension analysis yields values ranging from -0.45 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.19, maximum 1.00, mean 0.69, and standard deviation 0.37. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.03%, maximum percentage of 0.89%, average percentage of 0.28%, and standard deviation percentage of 0.41%.
Among the categorical predictors, the count of symbols ranges from 33 to 68 with a minimum entropy value 1.6941831940027274, maximum entropy 5.29992249720566, mean 4.016839249218758, and standard deviation 1.4208273530781312,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1016-21-1-3-classification.csv | A multivariate classification time-series dataset consists of 7212 samples and 8 features with 5 numerical and 3 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 7 to 7 with mean 7.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7212 samples the target ground-truth class has changed 292 times representing a percentage of 4.07%. There are 8 features in the dataset with a ratio of numerical to categorical features of 1.6666666666666667.
Among the numerical predictors, the series has 5 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.185. to 1.083 and kurtosis values of 0.37 to 0.77. The fractal dimension analysis yields values ranging from -0.39 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.37, maximum 1.00, mean 0.37, and standard deviation 0.55. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 1.48%, average percentage of 0.63%, and standard deviation percentage of 0.70%.
Among the categorical predictors, the count of symbols ranges from 36 to 54 with a minimum entropy value 1.2035031707424027, maximum entropy 5.066013644598859, mean 3.3581332038551874, and standard deviation 1.6081947827453784,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-12-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 4958 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 4958 samples the target ground-truth class has changed 1063 times representing a percentage of 21.59%. 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 0.023. to 10.459 and kurtosis values of 0.10 to 135.36. 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 -0.96, 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 11.37%, average percentage of 6.14%, and standard deviation percentage of 3.16%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 10, 'reg_lambda': 0.2} |
1031-47-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 7402 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 7402 samples the target ground-truth class has changed 1447 times representing a percentage of 19.64%. 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. 11 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.011. to 14.168 and kurtosis values of 0.10 to 241.90. 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 -0.79, maximum 1.00, mean 0.11, and standard deviation 0.51. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.29%, maximum percentage of 11.74%, average percentage of 5.72%, and standard deviation percentage of 4.48%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-17-1-1-3-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 1.45 showing a Unbalanced dataset. Among the 7409 samples the target ground-truth class has changed 1452 times representing a percentage of 19.69%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 13.421 and kurtosis values of 0.04 to 234.31. 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 -0.83, maximum 1.00, mean 0.10, and standard deviation 0.50. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 13.68%, average percentage of 6.51%, and standard deviation percentage of 4.23%.
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} |
1031-23-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 6806 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 6806 samples the target ground-truth class has changed 256 times representing a percentage of 3.78%. 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 0.064. to 82.218 and kurtosis values of 2.45 to 6763.88. The fractal dimension analysis yields values ranging from -0.83 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 -0.03, maximum 1.00, mean 0.71, and standard deviation 0.32. The count of numerical predictors with outliers is 16 with the minimum percentage of 20.01%, maximum percentage of 20.01%, average percentage of 20.01%, 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} |
1016-4-1-1-classification.csv | A multivariate classification time-series dataset consists of 7110 samples and 12 features with 4 numerical and 8 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 7110 samples the target ground-truth class has changed 349 times representing a percentage of 4.93%. 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 1 seasonality components detected in the numerical predictors. The top 1 common seasonality components are represented using sinusoidal waves. of periods 309 The numerical predictors also exhibit skewness values ranging from 0.080. to 1.047 and kurtosis values of 0.36 to 1.81. 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 0.24, maximum 1.00, mean 0.72, and standard deviation 0.35. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.23%, maximum percentage of 3.43%, average percentage of 1.12%, and standard deviation percentage of 1.55%.
Among the categorical predictors, the count of symbols ranges from 9 to 70 with a minimum entropy value 1.596924827405548, maximum entropy 5.341960327684085, mean 3.766577824750111, and standard deviation 1.4512353867626246,
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} |
1031-31-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7516 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 1.39 showing a Unbalanced dataset. Among the 7516 samples the target ground-truth class has changed 1260 times representing a percentage of 16.84%. 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. 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 0.136. to 16.831 and kurtosis values of 0.31 to 396.56. The fractal dimension analysis yields values ranging from -0.67 to -0.14 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.93, 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 0.07%, maximum percentage of 12.20%, average percentage of 7.46%, and standard deviation percentage of 3.12%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-29-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 7456 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.50 showing a Unbalanced dataset. Among the 7456 samples the target ground-truth class has changed 1659 times representing a percentage of 22.35%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.041. to 12.380 and kurtosis values of 0.10 to 188.07. The fractal dimension analysis yields values ranging from -0.62 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.13, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.07%, maximum percentage of 13.82%, average percentage of 4.73%, and standard deviation percentage of 4.28%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-6-1-1-4-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 213 times representing a percentage of 3.20%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.787. to 1.282 and kurtosis values of 2.40 to 3.80. The fractal dimension analysis yields values ranging from -0.16 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 0.60, maximum 1.00, mean 0.95, and standard deviation 0.12. The count of numerical predictors with outliers is 16 with the minimum percentage of 20.81%, maximum percentage of 20.81%, average percentage of 20.81%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1016-13-3-2-classification.csv | A multivariate classification time-series dataset consists of 6635 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 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 6635 samples the target ground-truth class has changed 210 times representing a percentage of 3.18%. There are 5 features in the dataset
Among the numerical predictors, the series has 5 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.131. to 0.746 and kurtosis values of 0.05 to 0.68. 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 -0.20, maximum 1.00, mean 0.49, and standard deviation 0.47. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 2.68%, average percentage of 0.72%, and standard deviation percentage of 1.15%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 20, 'n_estimators': 250} |
1031-12-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 6455 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 6455 samples the target ground-truth class has changed 1396 times representing a percentage of 21.74%. There are 16 features in the dataset
Among the numerical predictors, the series has 13 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.068. to 14.478 and kurtosis values of 0.13 to 295.35. 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 -0.93, maximum 1.00, mean 0.05, and standard deviation 0.51. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.05%, maximum percentage of 18.97%, average percentage of 6.54%, and standard deviation percentage of 5.44%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-41-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7619 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7619 samples the target ground-truth class has changed 1332 times representing a percentage of 17.56%. 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 0.045. to 12.835 and kurtosis values of 0.23 to 209.12. The fractal dimension analysis yields values ranging from -0.60 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.98, 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 3.77%, maximum percentage of 27.80%, average percentage of 17.56%, and standard deviation percentage of 7.01%.
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} |
1031-25-2-1-3-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 1.37 showing a Unbalanced dataset. Among the 7375 samples the target ground-truth class has changed 1170 times representing a percentage of 15.94%. 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 0.007. to 18.567 and kurtosis values of 0.53 to 520.08. 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 -0.92, maximum 1.00, mean 0.10, and standard deviation 0.47. The count of numerical predictors with outliers is 16 with the minimum percentage of 4.20%, maximum percentage of 30.05%, average percentage of 20.64%, and standard deviation percentage of 6.86%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1016-9-5-2-classification.csv | A multivariate classification time-series dataset consists of 7109 samples and 12 features with 5 numerical and 7 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 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 371 times representing a percentage of 5.24%. 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 11 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 507,646,1184 The numerical predictors also exhibit skewness values ranging from 0.137. to 1.265 and kurtosis values of 0.02 to 0.40. The fractal dimension analysis yields values ranging from -0.54 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.37, maximum 1.00, mean 0.31, and standard deviation 0.60. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.11%, maximum percentage of 22.12%, average percentage of 5.07%, and standard deviation percentage of 9.54%.
Among the categorical predictors, the count of symbols ranges from 9 to 60 with a minimum entropy value 0.9892260540811886, maximum entropy 5.42259578305675, mean 3.364251698794226, and standard deviation 1.5114677877355727,
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'} |
1016-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 7385 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.51 showing a Unbalanced dataset. Among the 7385 samples the target ground-truth class has changed 442 times representing a percentage of 6.01%. 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 9 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 410,461,923 The numerical predictors also exhibit skewness values ranging from 0.133. to 0.499 and kurtosis values of 0.02 to 0.44. The fractal dimension analysis yields values ranging from -0.44 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.15, maximum 1.00, mean 0.56, and standard deviation 0.54. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.07%, maximum percentage of 0.87%, average percentage of 0.31%, and standard deviation percentage of 0.38%.
Among the categorical predictors, the count of symbols ranges from 36 to 60 with a minimum entropy value 1.359705532484062, maximum entropy 5.26212024631263, mean 4.114669987749772, and standard deviation 1.5986212768220434,
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} |
1031-47-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7400 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.40 showing a Unbalanced dataset. Among the 7400 samples the target ground-truth class has changed 1382 times representing a percentage of 18.76%. 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 0.026. to 17.409 and kurtosis values of 0.14 to 398.28. 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 -0.83, 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 0.77%, maximum percentage of 21.42%, average percentage of 8.62%, and standard deviation percentage of 5.04%.
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} |
1031-57-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7479 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 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 7479 samples the target ground-truth class has changed 1513 times representing a percentage of 20.32%. 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 17.719 and kurtosis values of 0.07 to 398.44. The fractal dimension analysis yields values ranging from -0.64 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.95, maximum 1.00, mean 0.14, and standard deviation 0.55. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.07%, maximum percentage of 12.24%, average percentage of 4.93%, and standard deviation percentage of 3.88%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 40, 'n_estimators': 400} |
1031-28-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 7333 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 7333 samples the target ground-truth class has changed 1513 times representing a percentage of 20.73%. 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. 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 18.373 and kurtosis values of 0.14 to 491.07. The fractal dimension analysis yields values ranging from -0.72 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.95, maximum 1.00, mean 0.10, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.12%, maximum percentage of 12.77%, average percentage of 7.03%, and standard deviation percentage of 3.71%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-25-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7692 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 7692 samples the target ground-truth class has changed 496 times representing a percentage of 6.48%. 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. 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 0.155. to 87.446 and kurtosis values of 3.78 to 7650.51. The fractal dimension analysis yields values ranging from -0.81 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 -0.54, maximum 1.00, mean 0.34, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 21.39%, maximum percentage of 21.39%, average percentage of 21.39%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-44-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 7276 samples and 16 features with 12 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 1.36 showing a Unbalanced dataset. Among the 7276 samples the target ground-truth class has changed 70 times representing a percentage of 0.97%. There are 16 features in the dataset with a ratio of numerical to categorical features of 3.0.
Among the numerical predictors, the series has 12 numerical features detected as Stationary out of the 12 numerical features using the dickey-fuller test and the rest are Unstationary. 12 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 37.430. to 48.646 and kurtosis values of 1414.86 to 2380.32. The fractal dimension analysis yields values ranging from -0.33 to -0.03 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.67, maximum 1.00, mean 0.90, and standard deviation 0.10. The count of numerical predictors with outliers is 12 with the minimum percentage of 6.09%, maximum percentage of 6.09%, average percentage of 6.09%, and standard deviation percentage of 0.00%.
Among the categorical predictors, the count of symbols ranges from 87 to 167 with a minimum entropy value 0.44927759368527487, maximum entropy 0.7088761478755918, mean 0.6287706210907753, and standard deviation 0.10450917049740113,
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} |
1031-1-3-1-1-classification.csv | A multivariate classification time-series dataset consists of 7770 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 7770 samples the target ground-truth class has changed 375 times representing a percentage of 4.85%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.953. to 26.241 and kurtosis values of 3.28 to 690.75. The fractal dimension analysis yields values ranging from -0.56 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 0.03, maximum 1.00, mean 0.73, and standard deviation 0.31. The count of numerical predictors with outliers is 16 with the minimum percentage of 19.83%, maximum percentage of 19.83%, average percentage of 19.83%, 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} |
1031-14-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 7436 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 1.41 showing a Unbalanced dataset. Among the 7436 samples the target ground-truth class has changed 1325 times representing a percentage of 17.90%. 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 0.040. to 1.598 and kurtosis values of 0.03 to 3.10. The fractal dimension analysis yields values ranging from -0.68 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 -0.74, maximum 1.00, mean 0.19, and standard deviation 0.48. The count of numerical predictors with outliers is 14 with the minimum percentage of 0.00%, maximum percentage of 13.73%, average percentage of 7.19%, and standard deviation percentage of 4.32%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-54-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 6916 samples and 15 features with 15 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.48 showing a Unbalanced dataset. Among the 6916 samples the target ground-truth class has changed 1449 times representing a percentage of 21.05%. 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 0.024. to 1.680 and kurtosis values of 0.02 to 6.15. The fractal dimension analysis yields values ranging from -0.60 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.96, maximum 1.00, mean 0.11, and standard deviation 0.51. The count of numerical predictors with outliers is 14 with the minimum percentage of 0.00%, maximum percentage of 12.44%, average percentage of 4.82%, and standard deviation percentage of 3.86%.
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} |
1031-3-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 6404 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 6404 samples the target ground-truth class has changed 713 times representing a percentage of 11.19%. 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 0.180. to 13.137 and kurtosis values of 2.02 to 298.53. The fractal dimension analysis yields values ranging from -0.71 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 -0.80, maximum 1.00, mean 0.12, and standard deviation 0.45. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.67%, maximum percentage of 36.30%, average percentage of 34.01%, and standard deviation percentage of 8.39%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-18-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7396 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.43 showing a Unbalanced dataset. Among the 7396 samples the target ground-truth class has changed 1244 times representing a percentage of 16.90%. 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. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.005. to 14.332 and kurtosis values of 0.08 to 279.89. The fractal dimension analysis yields values ranging from -0.65 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.81, maximum 1.00, mean 0.13, and standard deviation 0.48. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 11.64%, average percentage of 6.56%, and standard deviation percentage of 3.66%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-7-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 5845 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 5845 samples the target ground-truth class has changed 349 times representing a percentage of 6.01%. There are 16 features in the dataset
Among the numerical predictors, the series has 1 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 0.277. to 4.415 and kurtosis values of 1.49 to 20.51. The fractal dimension analysis yields values ranging from -0.22 to -0.07 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.28, maximum 1.00, mean 0.91, and standard deviation 0.19. The count of numerical predictors with outliers is 16 with the minimum percentage of 24.57%, maximum percentage of 24.57%, average percentage of 24.57%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-19-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 7715 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 7715 samples the target ground-truth class has changed 432 times representing a percentage of 5.62%. 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. 9 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.033. to 2.562 and kurtosis values of 3.61 to 10.25. The fractal dimension analysis yields values ranging from -0.48 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 -0.52, maximum 1.00, mean 0.24, and standard deviation 0.50. The count of numerical predictors with outliers is 15 with the minimum percentage of 27.59%, maximum percentage of 27.59%, average percentage of 27.59%, and standard deviation percentage of 0.00%.
Among the categorical predictors, the count of symbols ranges from 54 to 54 with a minimum entropy value 0.20351019443312918, maximum entropy 0.20351019443312918, mean 0.20351019443312918, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 4728.708045015879, 'penalty': 'l1', 'solver': 'saga'} |
1016-19-2-5-classification.csv | A multivariate classification time-series dataset consists of 7209 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 7209 samples the target ground-truth class has changed 342 times representing a percentage of 4.76%. There are 8 features in the dataset with a ratio of numerical to categorical features of 1.0.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 3 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 53,225,240 The numerical predictors also exhibit skewness values ranging from 0.098. to 0.452 and kurtosis values of 0.25 to 0.57. The fractal dimension analysis yields values ranging from -0.38 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.04, maximum 1.00, mean 0.61, and standard deviation 0.49. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.10%, maximum percentage of 1.39%, average percentage of 0.55%, and standard deviation percentage of 0.58%.
Among the categorical predictors, the count of symbols ranges from 29 to 67 with a minimum entropy value 1.14062274247932, maximum entropy 5.214932190367139, mean 3.814918271550928, and standard deviation 1.6067637186206225,
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} |
1016-5-5-5-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 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 7108 samples the target ground-truth class has changed 271 times representing a percentage of 3.83%. There are 8 features in the dataset with a ratio of numerical to categorical features of 1.0.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.271. to 0.384 and kurtosis values of 0.34 to 0.69. The fractal dimension analysis yields values ranging from -0.38 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.00, maximum 1.00, mean 0.62, and standard deviation 0.47. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 1.55%, average percentage of 0.47%, and standard deviation percentage of 0.74%.
Among the categorical predictors, the count of symbols ranges from 27 to 63 with a minimum entropy value 1.6255165188337954, maximum entropy 5.065787454076257, mean 3.9063802001280203, and standard deviation 1.389370180671715,
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} |
1031-43-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 7628 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 1.44 showing a Unbalanced dataset. Among the 7628 samples the target ground-truth class has changed 1406 times representing a percentage of 18.51%. There are 13 features in the dataset
Among the numerical predictors, the series has 13 numerical features detected as Stationary out of the 13 numerical features using the dickey-fuller test and the rest are Unstationary. 12 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.000. to 1.564 and kurtosis values of 0.06 to 6.25. The fractal dimension analysis yields values ranging from -0.53 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 0.18, and standard deviation 0.54. The count of numerical predictors with outliers is 13 with the minimum percentage of 3.62%, maximum percentage of 11.19%, average percentage of 7.55%, and standard deviation percentage of 2.22%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1016-2-3-5-classification.csv | A multivariate classification time-series dataset consists of 7385 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 7385 samples the target ground-truth class has changed 173 times representing a percentage of 2.35%. 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 0.011. to 0.413 and kurtosis values of 0.13 to 0.97. The fractal dimension analysis yields values ranging from -0.46 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.14, maximum 1.00, mean 0.68, and standard deviation 0.40. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 0.86%, average percentage of 0.22%, and standard deviation percentage of 0.43%.
Among the categorical predictors, the count of symbols ranges from 36 to 55 with a minimum entropy value 1.636597703802923, maximum entropy 5.237282993693757, mean 4.211592264532429, and standard deviation 1.4904950111696533,
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} |
1020-59-2-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 739 times representing a percentage of 10.59%. 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 1168,1752,2336 The numerical predictors also exhibit skewness values ranging from 0.263. to 3.940 and kurtosis values of 0.32 to 23.95. The fractal dimension analysis yields values ranging from -0.77 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.82, maximum 1.00, mean 0.14, and standard deviation 0.47. The count of numerical predictors with outliers is 8 with the minimum percentage of 0.00%, maximum percentage of 9.42%, average percentage of 3.51%, and standard deviation percentage of 3.16%.
Among the categorical predictors, the count of symbols ranges from 17 to 61 with a minimum entropy value 0.3471731255060896, maximum entropy 4.004362178085031, mean 2.17576765179556, and standard deviation 1.8285945262894705,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-55-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 7087 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 7087 samples the target ground-truth class has changed 1075 times representing a percentage of 15.24%. 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. 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 0.006. to 1.430 and kurtosis values of 0.25 to 3.36. The fractal dimension analysis yields values ranging from -0.56 to -0.10 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.90, maximum 1.00, mean 0.14, and standard deviation 0.53. The count of numerical predictors with outliers is 15 with the minimum percentage of 10.65%, maximum percentage of 27.70%, average percentage of 21.06%, and standard deviation percentage of 4.82%.
Among the categorical predictors, the count of symbols ranges from 110 to 110 with a minimum entropy value 0.35936056623064083, maximum entropy 0.35936056623064083, mean 0.35936056623064083, 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} |
2009.csv | A multivariate classification time-series dataset consists of 7008 samples and 6 features with 3 numerical and 3 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.48 showing a Unbalanced dataset. Among the 7008 samples the target ground-truth class has changed 1508 times representing a percentage of 21.62%. There are 6 features in the dataset with a ratio of numerical to categorical features of 1.0.
Among the numerical predictors, the series has 3 numerical features detected as Stationary out of the 3 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.579. to 1.646 and kurtosis values of 0.50 to 1.27. The fractal dimension analysis yields values ranging from -0.72 to -0.37 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.33, maximum 1.00, mean 0.47, and standard deviation 0.49. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 20.16%, average percentage of 7.23%, and standard deviation percentage of 11.22%.
Among the categorical predictors, the count of symbols ranges from 2 to 24 with a minimum entropy value 0.858419045621641, maximum entropy 4.584960423934195, mean 2.7502153880607216, and standard deviation 1.5218889139825633,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 20, 'n_estimators': 100} |
1031-29-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 7448 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 7448 samples the target ground-truth class has changed 1611 times representing a percentage of 21.73%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 14.577 and kurtosis values of 0.02 to 248.25. 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 -0.93, 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 0.66%, maximum percentage of 14.30%, average percentage of 8.23%, and standard deviation percentage of 4.74%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1030-240-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 4140 samples the target ground-truth class has changed 40 times representing a percentage of 0.97%. 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 0.239. to 4.968 and kurtosis values of 0.00 to 49.79. The fractal dimension analysis yields values ranging from -0.58 to -0.31 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.47, maximum 1.00, mean 0.54, and standard deviation 0.65. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.70%, maximum percentage of 3.47%, average percentage of 1.41%, and standard deviation percentage of 1.18%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-43-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7628 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 7628 samples the target ground-truth class has changed 1405 times representing a percentage of 18.50%. 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 0.011. to 15.044 and kurtosis values of 0.09 to 269.80. The fractal dimension analysis yields values ranging from -0.55 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 3.11%, maximum percentage of 12.85%, average percentage of 7.39%, and standard deviation percentage of 3.04%.
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} |
3001-47.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 1.21 showing a Unbalanced dataset. Among the 720 samples the target ground-truth class has changed 435 times representing a percentage of 61.44%. 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 0.002. to 0.002 and kurtosis values of 0.73 to 0.73. The fractal dimension analysis yields values ranging from -1.39 to -1.39 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 1.00, maximum 1.00, mean 1.00, and standard deviation 0.00. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.85%, maximum percentage of 0.85%, average percentage of 0.85%,
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} |
1031-28-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 7623 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 7623 samples the target ground-truth class has changed 1574 times representing a percentage of 20.74%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.016. to 13.656 and kurtosis values of 0.07 to 245.65. The fractal dimension analysis yields values ranging from -0.64 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.10, and standard deviation 0.46. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.20%, maximum percentage of 15.50%, average percentage of 7.46%, and standard deviation percentage of 4.31%.
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} |
1030-278-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 55 times representing a percentage of 1.33%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.199. to 3.104 and kurtosis values of 0.23 to 27.48. The fractal dimension analysis yields values ranging from -0.62 to -0.29 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.45, maximum 1.00, mean 0.51, and standard deviation 0.64. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 4.78%, average percentage of 1.02%, and standard deviation percentage of 2.10%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-24-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 6694 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 6694 samples the target ground-truth class has changed 1032 times representing a percentage of 15.50%. 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 0.095. to 11.882 and kurtosis values of 0.97 to 185.74. The fractal dimension analysis yields values ranging from -0.56 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.13, and standard deviation 0.47. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.68%, maximum percentage of 32.79%, average percentage of 25.11%, and standard deviation percentage of 8.13%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-43-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7353 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 7353 samples the target ground-truth class has changed 1143 times representing a percentage of 15.62%. 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 0.028. to 16.876 and kurtosis values of 0.60 to 373.11. 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 -0.94, maximum 1.00, mean 0.17, and standard deviation 0.50. The count of numerical predictors with outliers is 15 with the minimum percentage of 2.27%, maximum percentage of 46.70%, average percentage of 34.84%, and standard deviation percentage of 15.63%.
Among the categorical predictors, the count of symbols ranges from 70 to 70 with a minimum entropy value 0.35135139132440385, maximum entropy 0.35135139132440385, mean 0.35135139132440385, 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'} |
1031-8-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7748 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 7748 samples the target ground-truth class has changed 1724 times representing a percentage of 22.35%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.049. to 13.878 and kurtosis values of 0.10 to 247.09. The fractal dimension analysis yields values ranging from -0.62 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.87, maximum 1.00, mean 0.14, and standard deviation 0.47. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.23%, maximum percentage of 14.66%, average percentage of 8.42%, and standard deviation percentage of 5.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1016-11-6-1-classification.csv | A multivariate classification time-series dataset consists of 7110 samples and 12 features with 5 numerical and 7 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7110 samples the target ground-truth class has changed 342 times representing a percentage of 4.83%. 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 4 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 309,355,3555 The numerical predictors also exhibit skewness values ranging from 0.012. to 1.392 and kurtosis values of 0.01 to 0.88. The fractal dimension analysis yields values ranging from -0.58 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 -0.38, maximum 1.00, mean 0.38, and standard deviation 0.56. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 20.72%, average percentage of 4.86%, and standard deviation percentage of 8.94%.
Among the categorical predictors, the count of symbols ranges from 9 to 65 with a minimum entropy value 1.5545104403698442, maximum entropy 4.844410707619148, mean 3.453121332452708, and standard deviation 1.3411105833529384,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1028-7-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 4 classes with entropy value 1.94 showing a Unbalanced dataset. Among the 6231 samples the target ground-truth class has changed 38 times representing a percentage of 0.61%. There are 8 features in the dataset
Among the numerical predictors, the series has 2 numerical features detected as Stationary out of the 8 numerical features using the dickey-fuller test and the rest are Unstationary. 8 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.055. to 11.120 and kurtosis values of 1.00 to 278.15. The fractal dimension analysis yields values ranging from -0.64 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 -0.32, maximum 1.00, mean 0.60, and standard deviation 0.52. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 7.71%, average percentage of 1.65%, and standard deviation percentage of 3.11%.
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': 250} |
1016-19-3-4-classification.csv | A multivariate classification time-series dataset consists of 7210 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 7210 samples the target ground-truth class has changed 220 times representing a percentage of 3.07%. 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 0.210. to 0.463 and kurtosis values of 0.24 to 0.54. The fractal dimension analysis yields values ranging from -0.36 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 0.05, maximum 1.00, mean 0.64, and standard deviation 0.44. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.01%, maximum percentage of 2.51%, average percentage of 0.65%, and standard deviation percentage of 1.24%.
Among the categorical predictors, the count of symbols ranges from 30 to 65 with a minimum entropy value 1.2094437513066898, maximum entropy 5.399176766448163, mean 3.861427441133788, and standard deviation 1.6106912580715986,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 100.0, 'l1_ratio': 0.00055, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-26-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 7593 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7593 samples the target ground-truth class has changed 1304 times representing a percentage of 17.25%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.041. to 13.359 and kurtosis values of 0.09 to 231.00. The fractal dimension analysis yields values ranging from -0.67 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.91, maximum 1.00, mean 0.14, and standard deviation 0.46. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.05%, maximum percentage of 11.92%, average percentage of 6.42%, 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} |
1031-51-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 6707 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 6707 samples the target ground-truth class has changed 1174 times representing a percentage of 17.59%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.009. to 15.549 and kurtosis values of 0.21 to 324.85. The fractal dimension analysis yields values ranging from -0.68 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.95, maximum 1.00, mean 0.10, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.63%, maximum percentage of 25.72%, average percentage of 17.34%, and standard deviation percentage of 6.82%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 181.8181818181818, 'l1_ratio': 0.00055, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-35-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 7614 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 7614 samples the target ground-truth class has changed 1554 times representing a percentage of 20.50%. 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 0.016. to 10.915 and kurtosis values of 0.14 to 166.70. 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 0.10, and standard deviation 0.50. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 15.57%, average percentage of 8.88%, and standard deviation percentage of 4.49%.
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} |
1031-24-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 7469 samples and 15 features with 15 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 7469 samples the target ground-truth class has changed 281 times representing a percentage of 3.78%. There are 15 features in the dataset
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 2.012. to 21.801 and kurtosis values of 8.97 to 479.30. The fractal dimension analysis yields values ranging from -0.31 to -0.03 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 0.87, and standard deviation 0.27. The count of numerical predictors with outliers is 15 with the minimum percentage of 14.39%, maximum percentage of 14.39%, average percentage of 14.39%, 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} |
1016-2-3-1-classification.csv | A multivariate classification time-series dataset consists of 7385 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7385 samples the target ground-truth class has changed 290 times representing a percentage of 3.95%. There are 8 features in the dataset with a ratio of numerical to categorical features of 1.0.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 5 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 144,184,199 The numerical predictors also exhibit skewness values ranging from 0.012. to 0.428 and kurtosis values of 0.44 to 0.79. The fractal dimension analysis yields values ranging from -0.44 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.21, maximum 1.00, mean 0.54, and standard deviation 0.57. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 0.05%, average percentage of 0.03%, and standard deviation percentage of 0.02%.
Among the categorical predictors, the count of symbols ranges from 36 to 64 with a minimum entropy value 1.5265535901563103, maximum entropy 5.335609862202225, mean 4.1896379489691835, and standard deviation 1.5456974193858193,
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'} |
1031-21-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 6940 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 6940 samples the target ground-truth class has changed 1295 times representing a percentage of 18.75%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.001. to 14.202 and kurtosis values of 0.00 to 269.38. The fractal dimension analysis yields values ranging from -0.62 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.12, and standard deviation 0.47. The count of numerical predictors with outliers is 16 with the minimum percentage of 3.23%, maximum percentage of 22.34%, average percentage of 12.75%, and standard deviation percentage of 6.46%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1030-439-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 16 times representing a percentage of 0.39%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 1.107. to 19.713 and kurtosis values of 0.02 to 567.07. 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 0.17, maximum 1.00, mean 0.73, and standard deviation 0.39. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.82%, maximum percentage of 5.85%, average percentage of 1.84%, and standard deviation percentage of 2.24%.
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'} |
1031-6-3-1-1-classification.csv | A multivariate classification time-series dataset consists of 6649 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 6649 samples the target ground-truth class has changed 1292 times representing a percentage of 19.53%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.023. to 16.782 and kurtosis values of 0.16 to 403.23. 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 -0.89, maximum 1.00, mean 0.11, and standard deviation 0.52. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 14.35%, average percentage of 6.28%, and standard deviation percentage of 4.83%.
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} |
1031-59-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7028 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 7028 samples the target ground-truth class has changed 1587 times representing a percentage of 22.69%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.006. to 17.985 and kurtosis values of 0.02 to 538.13. The fractal dimension analysis yields values ranging from -0.74 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.13, and standard deviation 0.49. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 16.04%, average percentage of 5.04%, and standard deviation percentage of 4.62%.
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} |
1031-43-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 7625 samples and 14 features with 14 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.36 showing a Unbalanced dataset. Among the 7625 samples the target ground-truth class has changed 977 times representing a percentage of 12.87%. There are 14 features in the dataset
Among the numerical predictors, the series has 14 numerical features detected as Stationary out of the 14 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.053. to 18.127 and kurtosis values of 0.99 to 434.79. The fractal dimension analysis yields values ranging from -0.76 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.93, maximum 1.00, mean 0.14, and standard deviation 0.51. The count of numerical predictors with outliers is 14 with the minimum percentage of 2.58%, maximum percentage of 48.31%, average percentage of 40.77%, and standard deviation percentage of 13.29%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1030-503-classification.csv | A multivariate classification time-series dataset consists of 3241 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.42 showing a Unbalanced dataset. Among the 3241 samples the target ground-truth class has changed 14 times representing a percentage of 0.43%. 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 0.948. to 6.652 and kurtosis values of 0.15 to 71.56. 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 -0.36, maximum 1.00, mean 0.57, and standard deviation 0.63. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 4.93%, average percentage of 0.99%, and standard deviation percentage of 2.20%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
3001-12.csv | A multivariate classification time-series dataset consists of 216 samples and 4 features with 4 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 216 samples the target ground-truth class has changed 2 times representing a percentage of 1.01%. There are 4 features in the dataset
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 3 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.127. to 1.278 and kurtosis values of 0.14 to 1.94. The fractal dimension analysis yields values ranging from -0.82 to -0.48 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 0.10, and standard deviation 0.78. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 10.55%, average percentage of 4.77%, and standard deviation percentage of 5.57%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1016-5-5-2-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 200 to 200 with mean 200.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.51 showing a Unbalanced dataset. Among the 7108 samples the target ground-truth class has changed 626 times representing a percentage of 9.10%. 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 17 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 460,531,863 The numerical predictors also exhibit skewness values ranging from 0.113. to 0.577 and kurtosis values of 0.11 to 0.43. The fractal dimension analysis yields values ranging from -0.39 to -0.08 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.10, maximum 1.00, mean 0.58, and standard deviation 0.52. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.17%, maximum percentage of 1.54%, average percentage of 0.83%, and standard deviation percentage of 0.59%.
Among the categorical predictors, the count of symbols ranges from 38 to 69 with a minimum entropy value 1.8740780905991914, maximum entropy 5.203563271188427, mean 4.005298585278228, and standard deviation 1.2881831796470353,
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} |
1031-6-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 6686 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 6686 samples the target ground-truth class has changed 232 times representing a percentage of 3.49%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 1.341. to 57.533 and kurtosis values of 5.30 to 3313.89. The fractal dimension analysis yields values ranging from -0.82 to -0.07 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.03, maximum 1.00, mean 0.75, and standard deviation 0.29. The count of numerical predictors with outliers is 16 with the minimum percentage of 17.15%, maximum percentage of 17.15%, average percentage of 17.15%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 4728.708045015879, 'penalty': 'l1', 'solver': 'saga'} |
1031-60-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 6516 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 1.44 showing a Unbalanced dataset. Among the 6516 samples the target ground-truth class has changed 1364 times representing a percentage of 21.04%. There are 13 features in the dataset
Among the numerical predictors, the series has 13 numerical features detected as Stationary out of the 13 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.164. to 13.573 and kurtosis values of 0.06 to 235.46. The fractal dimension analysis yields values ranging from -0.58 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.95, maximum 1.00, mean 0.14, and standard deviation 0.53. The count of numerical predictors with outliers is 13 with the minimum percentage of 2.10%, maximum percentage of 14.72%, average percentage of 7.42%, and standard deviation percentage of 4.27%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=1), 'learning_rate': 0.1, 'n_estimators': 50} |
1016-22-1-2-classification.csv | A multivariate classification time-series dataset consists of 7110 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 1.47 showing a Unbalanced dataset. Among the 7110 samples the target ground-truth class has changed 619 times representing a percentage of 8.75%. 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 1 seasonality components detected in the numerical predictors. The top 1 common seasonality components are represented using sinusoidal waves. of periods 54 The numerical predictors also exhibit skewness values ranging from 0.222. to 7.603 and kurtosis values of 0.15 to 79.34. The fractal dimension analysis yields values ranging from -0.61 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 -0.35, maximum 1.00, mean 0.19, and standard deviation 0.44. The count of numerical predictors with outliers is 7 with the minimum percentage of 0.00%, maximum percentage of 7.46%, average percentage of 1.96%, and standard deviation percentage of 2.58%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1016-2-2-3-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 53 to 53 with mean 53.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.98 showing a Balanced dataset. Among the 7385 samples the target ground-truth class has changed 276 times representing a percentage of 3.78%. There are 8 features in the dataset with a ratio of numerical to categorical features of 1.0.
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 4 numerical features using the dickey-fuller test and the rest are Unstationary. 3 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 3 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 23,107,198 The numerical predictors also exhibit skewness values ranging from 0.011. to 0.268 and kurtosis values of 0.17 to 0.48. The fractal dimension analysis yields values ranging from -0.48 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 -0.16, maximum 1.00, mean 0.55, and standard deviation 0.55. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.08%, maximum percentage of 0.70%, average percentage of 0.35%, and standard deviation percentage of 0.28%.
Among the categorical predictors, the count of symbols ranges from 44 to 66 with a minimum entropy value 1.3588314366116754, maximum entropy 5.31705804560525, mean 4.116520268723916, and standard deviation 1.606187495824021,
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} |
1016-17-6-3-classification.csv | A multivariate classification time-series dataset consists of 7109 samples and 8 features with 4 numerical and 4 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.98 showing a Unbalanced dataset. Among the 7109 samples the target ground-truth class has changed 147 times representing a percentage of 2.08%. 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 0.014. to 0.465 and kurtosis values of 0.14 to 0.81. The fractal dimension analysis yields values ranging from -0.44 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 0.07, maximum 1.00, mean 0.65, and standard deviation 0.44. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.00%, maximum percentage of 0.33%, average percentage of 0.09%, and standard deviation percentage of 0.16%.
Among the categorical predictors, the count of symbols ranges from 36 to 56 with a minimum entropy value 1.801424474740429, maximum entropy 5.124957330989481, mean 4.158379829897594, and standard deviation 1.3658611508342995,
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} |
1028-32-classification.csv | A multivariate classification time-series dataset consists of 6231 samples and 8 features with 8 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.42 showing a Unbalanced dataset. Among the 6231 samples the target ground-truth class has changed 29 times representing a percentage of 0.47%. 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 1.364. to 4.182 and kurtosis values of 1.85 to 45.10. The fractal dimension analysis yields values ranging from -0.64 to -0.28 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.15, maximum 1.00, mean 0.70, and standard deviation 0.43. The count of numerical predictors with outliers is 8 with the minimum percentage of 4.84%, maximum percentage of 9.78%, average percentage of 8.58%, and standard deviation percentage of 2.02%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-46-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 7395 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 7395 samples the target ground-truth class has changed 1338 times representing a percentage of 18.18%. 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 0.069. to 1.406 and kurtosis values of 0.06 to 4.20. The fractal dimension analysis yields values ranging from -0.55 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.88, maximum 1.00, mean 0.09, and standard deviation 0.55. The count of numerical predictors with outliers is 15 with the minimum percentage of 1.02%, maximum percentage of 15.62%, average percentage of 9.74%, and standard deviation percentage of 4.29%.
Among the categorical predictors, the count of symbols ranges from 91 to 91 with a minimum entropy value 0.44896692135593874, maximum entropy 0.44896692135593874, mean 0.44896692135593874, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-51-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 6723 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 6723 samples the target ground-truth class has changed 1353 times representing a percentage of 20.23%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.001. to 13.029 and kurtosis values of 0.02 to 234.33. The fractal dimension analysis yields values ranging from -0.67 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.95, 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 14.31%, average percentage of 7.30%, and standard deviation percentage of 3.63%.
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} |
1031-50-1-1-6-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 1.46 showing a Unbalanced dataset. Among the 6768 samples the target ground-truth class has changed 1348 times representing a percentage of 20.02%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.036. to 16.106 and kurtosis values of 0.06 to 342.30. 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 -0.90, 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 14.35%, average percentage of 6.19%, and standard deviation percentage of 4.16%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-89-1-3-classification.csv | A multivariate classification time-series dataset consists of 6998 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 6998 samples the target ground-truth class has changed 1546 times representing a percentage of 22.20%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.026. to 15.742 and kurtosis values of 0.09 to 305.62. 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 -0.89, maximum 1.00, mean 0.12, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.50%, maximum percentage of 13.45%, average percentage of 7.06%, and standard deviation percentage of 4.91%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
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