dataset_name stringlengths 8 32 | series_description stringlengths 1.32k 2.25k | algorithm stringclasses 8 values | hyperparameters stringclasses 93 values |
|---|---|---|---|
1031-48-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7440 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.40 showing a Unbalanced dataset. Among the 7440 samples the target ground-truth class has changed 1499 times representing a percentage of 20.24%. 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 0.061. to 13.245 and kurtosis values of 0.18 to 235.62. 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 -0.97, maximum 1.00, mean 0.11, and standard deviation 0.48. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 14.04%, average percentage of 7.69%, and standard deviation percentage of 3.80%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1031-15-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7200 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 7200 samples the target ground-truth class has changed 1568 times representing a percentage of 21.88%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.105. to 14.711 and kurtosis values of 0.03 to 302.22. 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 -0.96, maximum 1.00, mean 0.11, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.52%, maximum percentage of 15.56%, average percentage of 6.63%, and standard deviation percentage of 4.14%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-42-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7352 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 7352 samples the target ground-truth class has changed 1714 times representing a percentage of 23.42%. 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 0.177. to 8.654 and kurtosis values of 0.10 to 100.83. 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 -0.97, maximum 1.00, mean 0.13, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.46%, maximum percentage of 13.01%, average percentage of 7.04%, and standard deviation percentage of 3.79%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1016-9-5-4-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 264 times representing a percentage of 3.73%. 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 2 seasonality components detected in the numerical predictors. The top 2 common seasonality components are represented using sinusoidal waves. of periods 68,215 The numerical predictors also exhibit skewness values ranging from 0.001. to 1.000 and kurtosis values of 0.43 to 0.96. 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 0.48, and standard deviation 0.48. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 2.04%, average percentage of 0.41%, and standard deviation percentage of 0.91%.
Among the categorical predictors, the count of symbols ranges from 9 to 63 with a minimum entropy value 1.0579356178656951, maximum entropy 5.456050264411655, mean 3.4058666636369153, and standard deviation 1.5117356934850485,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1030-332-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 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 1.547. to 18.253 and kurtosis values of 1.45 to 582.60. The fractal dimension analysis yields values ranging from -0.64 to -0.35 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.10, maximum 1.00, mean 0.65, and standard deviation 0.51. The count of numerical predictors with outliers is 5 with the minimum percentage of 5.00%, maximum percentage of 8.61%, average percentage of 7.72%, and standard deviation percentage of 1.53%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1020-65-3-classification.csv | A multivariate classification time-series dataset consists of 7012 samples and 11 features with 9 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 7012 samples the target ground-truth class has changed 683 times representing a percentage of 9.79%. 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 24 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 637,701,1168 The numerical predictors also exhibit skewness values ranging from 0.342. to 2.473 and kurtosis values of 0.09 to 9.12. The fractal dimension analysis yields values ranging from -0.63 to -0.14 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.82, maximum 1.00, mean 0.15, and standard deviation 0.49. The count of numerical predictors with outliers is 9 with the minimum percentage of 0.01%, maximum percentage of 8.50%, average percentage of 3.84%, and standard deviation percentage of 3.08%.
Among the categorical predictors, the count of symbols ranges from 17 to 59 with a minimum entropy value 0.5953887262341956, maximum entropy 3.6560631809142823, mean 2.1257259535742388, and standard deviation 1.5303372273400433,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1031-36-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7667 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.42 showing a Unbalanced dataset. Among the 7667 samples the target ground-truth class has changed 451 times representing a percentage of 5.91%. 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 0.016. to 11.069 and kurtosis values of 0.33 to 122.62. 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 -0.47, maximum 1.00, mean 0.50, and standard deviation 0.42. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.76%, maximum percentage of 32.01%, average percentage of 17.24%, and standard deviation percentage of 15.59%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-23-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7056 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 7056 samples the target ground-truth class has changed 1210 times representing a percentage of 17.23%. 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. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.035. to 15.549 and kurtosis values of 0.45 to 292.62. 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 -0.95, maximum 1.00, mean 0.09, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.49%, maximum percentage of 31.60%, average percentage of 23.31%, and standard deviation percentage of 8.17%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1030-212-classification.csv | A multivariate classification time-series dataset consists of 358 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 358 samples the target ground-truth class has changed 8 times representing a percentage of 2.29%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.253. to 7.332 and kurtosis values of 0.54 to 76.27. The fractal dimension analysis yields values ranging from -0.68 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 -0.15, maximum 1.00, mean 0.64, and standard deviation 0.52. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 6.59%, average percentage of 1.32%, and standard deviation percentage of 2.95%.
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': 50} |
3001-96.csv | A multivariate classification time-series dataset consists of 288 samples and 2 features with 2 numerical and 0 categorical features. Each instance has a window length of 4. The dataset has a sampling rate of 360.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.50 showing a Unbalanced dataset. Among the 288 samples the target ground-truth class has changed 273 times representing a percentage of 99.64%. There are 2 features in the dataset
Among the numerical predictors, the series has 2 numerical features detected as Stationary out of the 2 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.018. to 0.875 and kurtosis values of 0.15 to 0.85. The fractal dimension analysis yields values ranging from -1.88 to -1.18 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.56, maximum 1.00, mean 0.78, and standard deviation 0.22. The count of numerical predictors with outliers is 2 with the minimum percentage of 0.36%, maximum percentage of 30.66%, average percentage of 15.51%, and standard deviation percentage of 21.42%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 40, 'n_estimators': 100} |
1031-31-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 7769 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 7769 samples the target ground-truth class has changed 1141 times representing a percentage of 14.75%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.140. to 16.573 and kurtosis values of 0.10 to 391.19. The fractal dimension analysis yields values ranging from -0.66 to -0.16 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.93, 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 2.40%, maximum percentage of 21.45%, average percentage of 12.37%, and standard deviation percentage of 4.80%.
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} |
3001-84.csv | A multivariate classification time-series dataset consists of 648 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 1.26 showing a Unbalanced dataset. Among the 648 samples the target ground-truth class has changed 425 times representing a percentage of 66.82%. 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 0.053. to 0.053 and kurtosis values of 0.81 to 0.81. The fractal dimension analysis yields values ranging from -1.41 to -1.41 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 1.00, maximum 1.00, mean 1.00, and standard deviation 0.00. The count of numerical predictors with outliers is 0 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=1), 'learning_rate': 0.01, 'n_estimators': 100} |
1016-18-2-5-classification.csv | A multivariate classification time-series dataset consists of 7209 samples and 8 features with 5 numerical and 3 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 7209 samples the target ground-truth class has changed 299 times representing a percentage of 4.17%. 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 0.143. to 1.411 and kurtosis values of 0.13 to 1.80. The fractal dimension analysis yields values ranging from -0.37 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 -0.43, maximum 1.00, mean 0.39, and standard deviation 0.54. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.14%, maximum percentage of 5.28%, average percentage of 1.48%, and standard deviation percentage of 2.21%.
Among the categorical predictors, the count of symbols ranges from 31 to 41 with a minimum entropy value 1.1913687257311745, maximum entropy 4.875123469878176, mean 3.3849231542108016, and standard deviation 1.5839912688656292,
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} |
1030-344-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.98 showing a Balanced dataset. Among the 4140 samples the target ground-truth class has changed 37 times representing a percentage of 0.90%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.207. to 2.527 and kurtosis values of 1.13 to 10.92. The fractal dimension analysis yields values ranging from -0.64 to -0.33 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.31, maximum 1.00, mean 0.58, and standard deviation 0.61. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.53%, average percentage of 1.11%, and standard deviation percentage of 2.47%.
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'} |
1020-35-1-classification.csv | A multivariate classification time-series dataset consists of 6992 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 6992 samples the target ground-truth class has changed 852 times representing a percentage of 12.24%. There are 11 features in the dataset with a ratio of numerical to categorical features of 4.5.
Among the numerical predictors, the series has 9 numerical features detected as Stationary out of the 9 numerical features using the dickey-fuller test and the rest are Unstationary. 9 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 39 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 1748,2330,3496 The numerical predictors also exhibit skewness values ranging from 0.028. to 3.155 and kurtosis values of 0.62 to 14.74. The fractal dimension analysis yields values ranging from -0.75 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 -0.74, maximum 1.00, mean 0.14, and standard deviation 0.47. The count of numerical predictors with outliers is 6 with the minimum percentage of 0.00%, maximum percentage of 10.64%, average percentage of 3.28%, and standard deviation percentage of 3.44%.
Among the categorical predictors, the count of symbols ranges from 17 to 57 with a minimum entropy value 0.4009512591270654, maximum entropy 3.890763316267035, mean 2.14585728769705, and standard deviation 1.7449060285699847,
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} |
1031-26-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7572 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 7572 samples the target ground-truth class has changed 1201 times representing a percentage of 15.93%. 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 15.364 and kurtosis values of 0.16 to 328.19. The fractal dimension analysis yields values ranging from -0.64 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.94, maximum 1.00, mean 0.11, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.03%, maximum percentage of 13.01%, average percentage of 8.53%, and standard deviation percentage of 3.44%.
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} |
2011-5.csv | A multivariate classification time-series dataset consists of 28051 samples and 8 features with 2 numerical and 6 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 9 classes with entropy value 1.52 showing a Unbalanced dataset. Among the 28051 samples the target ground-truth class has changed 4530 times representing a percentage of 16.17%. There are 8 features in the dataset with a ratio of numerical to categorical features of 0.3333333333333333.
Among the numerical predictors, the series has 2 numerical features detected as Stationary out of the 2 numerical features using the dickey-fuller test and the rest are Unstationary. 2 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 68 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 3506,5610,9350 The numerical predictors also exhibit skewness values ranging from 0.149. to 0.362 and kurtosis values of 0.65 to 1.13. The fractal dimension analysis yields values ranging from -0.68 to -0.16 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.14, maximum 1.00, mean 0.43, and standard deviation 0.57. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%, and standard deviation percentage of 0.00%.
Among the categorical predictors, the count of symbols ranges from 5 to 112 with a minimum entropy value 0.18773847756717665, maximum entropy 5.573371164509029, mean 2.8844558423395004, and standard deviation 1.9887916192424266,
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} |
1016-19-2-1-classification.csv | A multivariate classification time-series dataset consists of 7210 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 1.50 showing a Unbalanced dataset. Among the 7210 samples the target ground-truth class has changed 445 times representing a percentage of 6.20%. 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 10 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 212,225,360 The numerical predictors also exhibit skewness values ranging from 0.221. to 14.585 and kurtosis values of 0.19 to 359.62. The fractal dimension analysis yields values ranging from -0.61 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 -0.28, maximum 1.00, mean 0.23, and standard deviation 0.43. The count of numerical predictors with outliers is 8 with the minimum percentage of 0.03%, maximum percentage of 10.02%, average percentage of 4.08%, and standard deviation percentage of 3.99%.
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} |
1030-28-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 48 times representing a percentage of 1.16%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.325. to 4.019 and kurtosis values of 0.46 to 36.48. The fractal dimension analysis yields values ranging from -0.59 to -0.32 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.49, maximum 1.00, mean 0.53, and standard deviation 0.69. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.04%, average percentage of 1.01%, and standard deviation percentage of 2.26%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1030-80-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 1.45 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 42 times representing a percentage of 1.02%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 1.503. to 6.177 and kurtosis values of 2.57 to 73.08. The fractal dimension analysis yields values ranging from -0.57 to -0.30 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.51, maximum 1.00, mean 0.85, and standard deviation 0.22. The count of numerical predictors with outliers is 5 with the minimum percentage of 3.78%, maximum percentage of 5.07%, average percentage of 4.79%, and standard deviation percentage of 0.56%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1016-11-6-3-classification.csv | A multivariate classification time-series dataset consists of 7109 samples and 12 features with 5 numerical and 7 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7109 samples the target ground-truth class has changed 219 times representing a percentage of 3.09%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.7142857142857143.
Among the numerical predictors, the series has 5 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.036. to 2.031 and kurtosis values of 0.20 to 17.98. The fractal dimension analysis yields values ranging from -0.59 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 -0.35, maximum 1.00, mean 0.37, and standard deviation 0.54. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 22.02%, average percentage of 4.95%, and standard deviation percentage of 9.58%.
Among the categorical predictors, the count of symbols ranges from 9 to 59 with a minimum entropy value 1.5495715370567698, maximum entropy 4.933218376214293, mean 3.401937516086801, and standard deviation 1.3783633751274682,
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-40-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 6858 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 6858 samples the target ground-truth class has changed 429 times representing a percentage of 6.29%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.178. to 18.333 and kurtosis values of 3.04 to 481.99. The fractal dimension analysis yields values ranging from -0.64 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.96, maximum 1.00, mean 0.08, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.91%, maximum percentage of 30.60%, average percentage of 28.80%, and standard deviation percentage of 7.17%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1031-26-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7594 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.38 showing a Unbalanced dataset. Among the 7594 samples the target ground-truth class has changed 1285 times representing a percentage of 17.00%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.022. to 11.056 and kurtosis values of 0.05 to 162.15. The fractal dimension analysis yields values ranging from -0.63 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 -0.94, 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 0.41%, maximum percentage of 11.20%, average percentage of 6.94%, and standard deviation percentage of 3.00%.
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} |
3001-1.csv | A multivariate classification time-series dataset consists of 192 samples and 2 features with 2 numerical and 0 categorical features. Each instance has a window length of 4. The dataset has a sampling rate of 360.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.50 showing a Unbalanced dataset. Among the 192 samples the target ground-truth class has changed 177 times representing a percentage of 99.44%. There are 2 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 2 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.046. to 0.903 and kurtosis values of 0.45 to 0.69. The fractal dimension analysis yields values ranging from -1.90 to -1.64 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.63, maximum 1.00, mean 0.81, and standard deviation 0.19. The count of numerical predictors with outliers is 2 with the minimum percentage of 3.37%, maximum percentage of 47.19%, average percentage of 25.28%, and standard deviation percentage of 30.99%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=3), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-17-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 4922 samples and 16 features with 15 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.36 showing a Unbalanced dataset. Among the 4922 samples the target ground-truth class has changed 560 times representing a percentage of 11.46%. 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 0.114. to 1.075 and kurtosis values of 1.06 to 6.39. The fractal dimension analysis yields values ranging from -0.53 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 -0.81, maximum 1.00, mean 0.14, and standard deviation 0.51. The count of numerical predictors with outliers is 15 with the minimum percentage of 41.65%, maximum percentage of 44.50%, average percentage of 44.31%, and standard deviation percentage of 0.73%.
Among the categorical predictors, the count of symbols ranges from 38 to 38 with a minimum entropy value 0.17481230641363216, maximum entropy 0.17481230641363216, mean 0.17481230641363216, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-21-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7516 samples and 15 features with 15 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7516 samples the target ground-truth class has changed 762 times representing a percentage of 10.18%. There are 15 features in the dataset
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.081. to 2.893 and kurtosis values of 0.90 to 9.55. The fractal dimension analysis yields values ranging from -0.48 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 0.13, and standard deviation 0.53. The count of numerical predictors with outliers is 15 with the minimum percentage of 10.30%, maximum percentage of 40.40%, average percentage of 38.40%, and standard deviation percentage of 7.77%.
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} |
1031-59-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 6452 samples and 12 features with 12 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 6452 samples the target ground-truth class has changed 891 times representing a percentage of 13.88%. There are 12 features in the dataset
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. 10 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.008. to 18.678 and kurtosis values of 1.33 to 418.32. The fractal dimension analysis yields values ranging from -0.50 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 -0.91, maximum 1.00, mean 0.08, and standard deviation 0.76. The count of numerical predictors with outliers is 12 with the minimum percentage of 1.68%, maximum percentage of 41.84%, average percentage of 38.49%, and standard deviation percentage of 11.59%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-7-3-1-2-classification.csv | A multivariate classification time-series dataset consists of 6725 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 5274 with mean 329.62 and standard deviation 1318.50.
The target column has 3 classes with entropy value 1.35 showing a Unbalanced dataset. Among the 6725 samples the target ground-truth class has changed 292 times representing a percentage of 4.36%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.129. to 9.340 and kurtosis values of 7.18 to 198.03. The fractal dimension analysis yields values ranging from -0.55 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 0.05, and standard deviation 0.59. The count of numerical predictors with outliers is 16 with the minimum percentage of 18.77%, maximum percentage of 28.71%, average percentage of 19.39%, and standard deviation percentage of 2.48%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1030-82-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 48 times representing a percentage of 1.16%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 5 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 1.702. to 9.555 and kurtosis values of 2.33 to 203.97. The fractal dimension analysis yields values ranging from -0.64 to -0.32 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.25, maximum 1.00, mean 0.60, and standard deviation 0.58. The count of numerical predictors with outliers is 5 with the minimum percentage of 5.14%, maximum percentage of 8.15%, average percentage of 7.49%, and standard deviation percentage of 1.32%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1016-11-2-3-classification.csv | A multivariate classification time-series dataset consists of 7109 samples and 12 features with 4 numerical and 8 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 7109 samples the target ground-truth class has changed 312 times representing a percentage of 4.41%. 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 3 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 78,139,165 The numerical predictors also exhibit skewness values ranging from 0.198. to 0.750 and kurtosis values of 0.05 to 0.45. The fractal dimension analysis yields values ranging from -0.46 to -0.10 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.00, maximum 1.00, mean 0.62, and standard deviation 0.46. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.10%, maximum percentage of 1.22%, average percentage of 0.46%, and standard deviation percentage of 0.51%.
Among the categorical predictors, the count of symbols ranges from 9 to 70 with a minimum entropy value 1.326785398441256, maximum entropy 5.218621138334966, mean 3.711111178663709, and standard deviation 1.5052328895395912,
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'} |
1031-10-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 6651 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 6651 samples the target ground-truth class has changed 1115 times representing a percentage of 16.85%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.014. to 17.110 and kurtosis values of 0.51 to 364.56. The fractal dimension analysis yields values ranging from -0.67 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.96, maximum 1.00, mean 0.11, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.83%, maximum percentage of 49.12%, average percentage of 36.74%, and standard deviation percentage of 12.07%.
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 99999.99999999999, 'penalty': 'l1', 'solver': 'saga'} |
1030-26-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 14 times representing a percentage of 0.34%. 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 0.052. to 6.015 and kurtosis values of 1.44 to 61.90. The fractal dimension analysis yields values ranging from -0.63 to -0.31 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.21, maximum 1.00, mean 0.61, and standard deviation 0.56. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 12.22%, average percentage of 2.44%, and standard deviation percentage of 5.47%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-58-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 6768 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 1.39 showing a Unbalanced dataset. Among the 6768 samples the target ground-truth class has changed 561 times representing a percentage of 8.33%. 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 1.320. to 20.264 and kurtosis values of 2.78 to 411.24. The fractal dimension analysis yields values ranging from -0.58 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 0.12, maximum 1.00, mean 0.81, and standard deviation 0.31. The count of numerical predictors with outliers is 13 with the minimum percentage of 0.24%, maximum percentage of 28.45%, average percentage of 2.48%, and standard deviation percentage of 7.81%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 181.8181818181818, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-41-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7602 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 7602 samples the target ground-truth class has changed 1543 times representing a percentage of 20.39%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 11.714 and kurtosis values of 0.10 to 167.28. The fractal dimension analysis yields values ranging from -0.67 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.11, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.39%, maximum percentage of 13.85%, average percentage of 7.48%, and standard deviation percentage of 3.40%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-31-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 7773 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 7773 samples the target ground-truth class has changed 1264 times representing a percentage of 16.33%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.030. to 14.532 and kurtosis values of 0.28 to 267.91. The fractal dimension analysis yields values ranging from -0.64 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 -0.89, maximum 1.00, mean 0.12, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.69%, maximum percentage of 16.69%, average percentage of 10.60%, and standard deviation percentage of 4.71%.
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} |
1031-26-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 7440 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7440 samples the target ground-truth class has changed 943 times representing a percentage of 12.73%. 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. 10 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.010. to 14.353 and kurtosis values of 1.18 to 289.62. The fractal dimension analysis yields values ranging from -0.63 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 -0.93, 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 3.42%, maximum percentage of 48.89%, average percentage of 41.02%, and standard deviation percentage of 14.66%.
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} |
1031-33-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7182 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 7182 samples the target ground-truth class has changed 1392 times representing a percentage of 19.47%. 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 0.034. to 13.888 and kurtosis values of 0.01 to 237.81. The fractal dimension analysis yields values ranging from -0.71 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.86, maximum 1.00, mean 0.08, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.58%, maximum percentage of 12.81%, average percentage of 7.08%, and standard deviation percentage of 3.79%.
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} |
1031-21-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7392 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 7392 samples the target ground-truth class has changed 1503 times representing a percentage of 20.43%. 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 0.003. to 19.805 and kurtosis values of 0.08 to 630.14. The fractal dimension analysis yields values ranging from -0.67 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.13, and standard deviation 0.48. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 15.94%, average percentage of 6.16%, and standard deviation percentage of 5.40%.
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} |
1030-157-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 5 classes with entropy value 2.29 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 74 times representing a percentage of 1.79%. 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 1.146. to 1.813 and kurtosis values of 1.20 to 6.05. The fractal dimension analysis yields values ranging from -0.61 to -0.33 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.39, maximum 1.00, mean 0.56, and standard deviation 0.65. The count of numerical predictors with outliers is 5 with the minimum percentage of 2.76%, maximum percentage of 3.49%, average percentage of 3.12%, and standard deviation percentage of 0.26%.
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 1000.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
1031-35-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 7608 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7608 samples the target ground-truth class has changed 885 times representing a percentage of 11.68%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 17.685 and kurtosis values of 1.27 to 381.74. 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 -0.97, 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 1.95%, maximum percentage of 41.46%, average percentage of 38.81%, and standard deviation percentage of 9.85%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-7-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 6729 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.41 showing a Unbalanced dataset. Among the 6729 samples the target ground-truth class has changed 1174 times representing a percentage of 17.54%. 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 0.015. to 16.082 and kurtosis values of 0.08 to 313.12. The fractal dimension analysis yields values ranging from -0.61 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.93, 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 0.85%, maximum percentage of 15.06%, average percentage of 9.19%, 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': 5, 'n_estimators': 50} |
1031-95-1-2-classification.csv | A multivariate classification time-series dataset consists of 5838 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 5838 samples the target ground-truth class has changed 1343 times representing a percentage of 23.14%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 13 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.023. to 10.742 and kurtosis values of 0.01 to 152.17. The fractal dimension analysis yields values ranging from -0.73 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.98, maximum 1.00, mean 0.11, and standard deviation 0.48. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.03%, maximum percentage of 13.85%, average percentage of 6.68%, and standard deviation percentage of 4.33%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1020-50-1-classification.csv | A multivariate classification time-series dataset consists of 7008 samples and 11 features with 9 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 3 classes with entropy value 1.45 showing a Unbalanced dataset. Among the 7008 samples the target ground-truth class has changed 828 times representing a percentage of 11.87%. There are 11 features in the dataset with a ratio of numerical to categorical features of 4.5.
Among the numerical predictors, the series has 9 numerical features detected as Stationary out of the 9 numerical features using the dickey-fuller test and the rest are Unstationary. 8 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 28 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 637,876,1752 The numerical predictors also exhibit skewness values ranging from 0.103. to 2.705 and kurtosis values of 0.18 to 10.97. 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 -0.72, maximum 1.00, mean 0.14, and standard deviation 0.45. The count of numerical predictors with outliers is 7 with the minimum percentage of 0.00%, maximum percentage of 9.82%, average percentage of 4.24%, and standard deviation percentage of 3.57%.
Among the categorical predictors, the count of symbols ranges from 17 to 48 with a minimum entropy value 0.31082755814930224, maximum entropy 3.8823839696765776, mean 2.09660576391294, and standard deviation 1.7857782057636378,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-58-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 6772 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 6772 samples the target ground-truth class has changed 1394 times representing a percentage of 20.69%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 12.980 and kurtosis values of 0.07 to 213.42. The fractal dimension analysis yields values ranging from -0.68 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.08, and standard deviation 0.49. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 11.16%, average percentage of 5.36%, and standard deviation percentage of 3.51%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-5-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7554 samples and 16 features with 15 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.44 showing a Unbalanced dataset. Among the 7554 samples the target ground-truth class has changed 1403 times representing a percentage of 18.66%. There are 16 features in the dataset with a ratio of numerical to categorical features of 15.0.
Among the numerical predictors, the series has 12 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.030. to 0.896 and kurtosis values of 0.01 to 2.30. The fractal dimension analysis yields values ranging from -0.60 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.86, maximum 1.00, mean 0.10, and standard deviation 0.55. The count of numerical predictors with outliers is 13 with the minimum percentage of 0.00%, maximum percentage of 16.20%, average percentage of 5.10%, and standard deviation percentage of 4.61%.
Among the categorical predictors, the count of symbols ranges from 97 to 97 with a minimum entropy value 0.4317908390508183, maximum entropy 0.4317908390508183, mean 0.4317908390508183, 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} |
1031-41-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7619 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7619 samples the target ground-truth class has changed 1299 times representing a percentage of 17.13%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.068. to 25.749 and kurtosis values of 0.35 to 806.83. 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 -0.98, maximum 1.00, mean 0.13, and standard deviation 0.53. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.39%, maximum percentage of 32.45%, average percentage of 24.27%, and standard deviation percentage of 7.63%.
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': 10, 'reg_lambda': 0.2} |
1031-52-2-1-6-classification.csv | A multivariate classification time-series dataset consists of 6776 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 6776 samples the target ground-truth class has changed 1334 times representing a percentage of 19.79%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.077. to 15.421 and kurtosis values of 0.04 to 308.50. The fractal dimension analysis yields values ranging from -0.69 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.92, maximum 1.00, mean 0.08, and standard deviation 0.49. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 13.25%, average percentage of 7.09%, and standard deviation percentage of 4.09%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1016-6-4-1-classification.csv | A multivariate classification time-series dataset consists of 6681 samples and 12 features with 11 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 1147 with mean 104.27 and standard deviation 345.83. Similarly, the missing values percentages for categorical features range from 1147 to 1147 with mean 1147.0
The target column has 3 classes with entropy value 1.52 showing a Unbalanced dataset. Among the 6681 samples the target ground-truth class has changed 620 times representing a percentage of 11.26%. There are 12 features in the dataset with a ratio of numerical to categorical features of 11.0.
Among the numerical predictors, the series has 11 numerical features detected as Stationary out of the 11 numerical features using the dickey-fuller test and the rest are Unstationary. 11 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 17 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 149,221,1106 The numerical predictors also exhibit skewness values ranging from 0.071. to 14.163 and kurtosis values of 0.08 to 347.22. The fractal dimension analysis yields values ranging from -0.63 to -0.35 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.56, maximum 1.00, mean 0.12, and standard deviation 0.40. The count of numerical predictors with outliers is 9 with the minimum percentage of 0.00%, maximum percentage of 15.11%, average percentage of 4.20%, and standard deviation percentage of 5.99%.
Among the categorical predictors, the count of symbols ranges from 9 to 9 with a minimum entropy value 2.6354026833928925, maximum entropy 2.6354026833928925, mean 2.6354026833928925, 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} |
1031-55-1-1-5-classification.csv | A multivariate classification time-series dataset consists of 7092 samples and 16 features with 15 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.44 showing a Unbalanced dataset. Among the 7092 samples the target ground-truth class has changed 1307 times representing a percentage of 18.52%. 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 0.004. to 1.465 and kurtosis values of 0.08 to 3.40. 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 -0.95, 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 0.17%, maximum percentage of 15.73%, average percentage of 6.24%, and standard deviation percentage of 5.40%.
Among the categorical predictors, the count of symbols ranges from 109 to 109 with a minimum entropy value 0.4914023345384199, maximum entropy 0.4914023345384199, mean 0.4914023345384199, 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} |
1031-1-3-1-5-classification.csv | A multivariate classification time-series dataset consists of 7288 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 7288 samples the target ground-truth class has changed 236 times representing a percentage of 3.25%. 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. 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 2.723. to 34.488 and kurtosis values of 12.33 to 1193.47. 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 0.06, maximum 1.00, mean 0.87, and standard deviation 0.28. The count of numerical predictors with outliers is 16 with the minimum percentage of 14.65%, maximum percentage of 14.65%, average percentage of 14.65%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-23-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7469 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 2671 with mean 166.94 and standard deviation 667.75.
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7469 samples the target ground-truth class has changed 1174 times representing a percentage of 15.79%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.010. to 43.476 and kurtosis values of 0.34 to 2576.66. The fractal dimension analysis yields values ranging from -0.61 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.94, maximum 1.00, mean 0.12, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 11.69%, maximum percentage of 32.94%, average percentage of 24.06%, and standard deviation percentage of 6.83%.
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} |
1031-55-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 7091 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 7091 samples the target ground-truth class has changed 1262 times representing a percentage of 17.88%. 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 0.030. to 1.151 and kurtosis values of 0.02 to 1.52. 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 -0.90, maximum 1.00, mean 0.13, and standard deviation 0.54. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.09%, maximum percentage of 14.67%, average percentage of 5.98%, and standard deviation percentage of 4.36%.
Among the categorical predictors, the count of symbols ranges from 93 to 93 with a minimum entropy value 0.3893512064770399, maximum entropy 0.3893512064770399, mean 0.3893512064770399, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-51-2-1-3-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 1.46 showing a Unbalanced dataset. Among the 7417 samples the target ground-truth class has changed 1493 times representing a percentage of 20.22%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.189. to 13.303 and kurtosis values of 0.01 to 211.57. 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 -0.93, maximum 1.00, mean 0.08, and standard deviation 0.50. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 15.33%, average percentage of 6.47%, and standard deviation percentage of 4.75%.
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} |
1030-387-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.98 showing a Balanced dataset. Among the 4140 samples the target ground-truth class has changed 13 times representing a percentage of 0.32%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.202. to 1.798 and kurtosis values of 0.83 to 6.10. The fractal dimension analysis yields values ranging from -0.65 to -0.30 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.15, maximum 1.00, mean 0.64, and standard deviation 0.52. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 4.29%, average percentage of 0.86%, and standard deviation percentage of 1.92%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-7-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 6729 samples and 15 features with 15 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.47 showing a Unbalanced dataset. Among the 6729 samples the target ground-truth class has changed 1265 times representing a percentage of 18.89%. There are 15 features in the dataset
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 14 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.014. to 1.411 and kurtosis values of 0.03 to 3.84. The fractal dimension analysis yields values ranging from -0.59 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.89, maximum 1.00, mean 0.13, and standard deviation 0.55. The count of numerical predictors with outliers is 13 with the minimum percentage of 0.00%, maximum percentage of 13.52%, average percentage of 5.59%, and standard deviation percentage of 4.92%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1034-3-7-classification.csv | A multivariate classification time-series dataset consists of 7963 samples and 6 features with 5 numerical and 1 categorical features. Each instance has a window length of 4. The dataset has a sampling rate of 15.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 7963 samples the target ground-truth class has changed 196 times representing a percentage of 2.47%. There are 6 features in the dataset with a ratio of numerical to categorical features of 5.0.
Among the numerical predictors, the series has 5 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 3 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in 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 1.206 and kurtosis values of 0.23 to 0.90. The fractal dimension analysis yields values ranging from -0.74 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 0.17, maximum 1.00, mean 0.55, and standard deviation 0.35. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 6.40%, average percentage of 2.25%, and standard deviation percentage of 2.91%.
Among the categorical predictors, the count of symbols ranges from 72 to 72 with a minimum entropy value 5.347454318724294, maximum entropy 5.347454318724294, mean 5.347454318724294, 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'} |
1031-61-1-2-classification.csv | A multivariate classification time-series dataset consists of 7664 samples and 15 features with 15 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7664 samples the target ground-truth class has changed 1274 times representing a percentage of 16.70%. 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. 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 0.004. to 18.133 and kurtosis values of 0.52 to 402.27. The fractal dimension analysis yields values ranging from -0.65 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.97, maximum 1.00, mean 0.10, and standard deviation 0.49. The count of numerical predictors with outliers is 15 with the minimum percentage of 2.48%, maximum percentage of 49.72%, average percentage of 37.43%, and standard deviation percentage of 15.91%.
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} |
3001-73.csv | A multivariate classification time-series dataset consists of 192 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 4. The dataset has a sampling rate of 360.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 192 samples the target ground-truth class has changed 177 times representing a percentage of 99.44%. There are 5 features in the dataset
Among the numerical predictors, the series has 3 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 1 seasonality components detected in the numerical predictors. The top 1 common seasonality components are represented using sinusoidal waves. of periods 27 The numerical predictors also exhibit skewness values ranging from 0.018. to 0.935 and kurtosis values of 0.15 to 0.88. The fractal dimension analysis yields values ranging from -2.01 to -1.17 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.50, maximum 1.00, mean 0.72, and standard deviation 0.20. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 15.73%, average percentage of 5.51%, and standard deviation percentage of 7.46%.
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} |
1031-61-1-6-classification.csv | A multivariate classification time-series dataset consists of 7663 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 1.37 showing a Unbalanced dataset. Among the 7663 samples the target ground-truth class has changed 1008 times representing a percentage of 13.21%. 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. 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 0.031. to 0.985 and kurtosis values of 1.14 to 8.28. The fractal dimension analysis yields values ranging from -0.52 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 0.17, and standard deviation 0.57. The count of numerical predictors with outliers is 12 with the minimum percentage of 43.92%, maximum percentage of 43.92%, average percentage of 43.92%, and standard deviation percentage of 0.00%.
Among the categorical predictors, the count of symbols ranges from 76 to 76 with a minimum entropy value 0.3391153324587429, maximum entropy 0.3391153324587429, mean 0.3391153324587429, 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} |
1031-51-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 6692 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 6692 samples the target ground-truth class has changed 1285 times representing a percentage of 19.30%. 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 0.218. to 2.770 and kurtosis values of 0.14 to 12.13. 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 -0.87, maximum 1.00, mean 0.09, and standard deviation 0.52. The count of numerical predictors with outliers is 14 with the minimum percentage of 0.00%, maximum percentage of 13.88%, average percentage of 7.69%, and standard deviation percentage of 4.18%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1028-45-classification.csv | A multivariate classification time-series dataset consists of 3778 samples and 10 features with 10 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 3778 samples the target ground-truth class has changed 14 times representing a percentage of 0.37%. There are 10 features in the dataset
Among the numerical predictors, the series has 4 numerical features detected as Stationary out of the 10 numerical features using the dickey-fuller test and the rest are Unstationary. 9 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.019. to 14.918 and kurtosis values of 0.49 to 317.41. The fractal dimension analysis yields values ranging from -0.68 to -0.30 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.23, maximum 1.00, mean 0.48, and standard deviation 0.49. The count of numerical predictors with outliers is 9 with the minimum percentage of 0.00%, maximum percentage of 14.70%, average percentage of 11.59%, and standard deviation percentage of 4.89%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=5), 'learning_rate': 0.01, 'n_estimators': 200} |
1031-15-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 7208 samples and 16 features with 15 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 6071 to 6071 with mean 6071.0
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7208 samples the target ground-truth class has changed 229 times representing a percentage of 20.67%. There are 16 features in the dataset with a ratio of numerical to categorical features of 15.0.
Among the numerical predictors, the series has 13 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.015. to 1.485 and kurtosis values of 0.28 to 4.13. The fractal dimension analysis yields values ranging from -0.73 to -0.16 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.98, maximum 1.00, mean 0.10, and standard deviation 0.50. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.00%, maximum percentage of 5.32%, average percentage of 1.08%, and standard deviation percentage of 1.76%.
Among the categorical predictors, the count of symbols ranges from 56 to 56 with a minimum entropy value 0.9650281822225228, maximum entropy 0.9650281822225228, mean 0.9650281822225228, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1031-40-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 7437 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 7437 samples the target ground-truth class has changed 424 times representing a percentage of 5.73%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 1.518. to 16.784 and kurtosis values of 4.81 to 283.65. The fractal dimension analysis yields values ranging from -0.34 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 0.17, maximum 1.00, mean 0.88, and standard deviation 0.26. The count of numerical predictors with outliers is 16 with the minimum percentage of 20.48%, maximum percentage of 20.48%, average percentage of 20.48%, 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} |
1031-19-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7713 samples and 15 features with 15 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 7713 samples the target ground-truth class has changed 1497 times representing a percentage of 19.49%. 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 0.013. to 18.262 and kurtosis values of 0.06 to 480.35. The fractal dimension analysis yields values ranging from -0.61 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.96, maximum 1.00, mean 0.13, and standard deviation 0.51. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.78%, maximum percentage of 14.92%, average percentage of 6.82%, and standard deviation percentage of 4.76%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-7-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 6711 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 6711 samples the target ground-truth class has changed 193 times representing a percentage of 2.89%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.119. to 5.554 and kurtosis values of 4.70 to 37.29. The fractal dimension analysis yields values ranging from -0.48 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 0.35, maximum 1.00, mean 0.90, and standard deviation 0.19. The count of numerical predictors with outliers is 16 with the minimum percentage of 14.44%, maximum percentage of 14.44%, average percentage of 14.44%, 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} |
1031-24-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 6689 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 6689 samples the target ground-truth class has changed 1265 times representing a percentage of 19.01%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.023. to 10.614 and kurtosis values of 0.09 to 137.96. The fractal dimension analysis yields values ranging from -0.69 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.96, maximum 1.00, mean 0.13, and standard deviation 0.49. The count of numerical predictors with outliers is 16 with the minimum percentage of 1.62%, maximum percentage of 11.77%, average percentage of 6.11%, and standard deviation percentage of 3.08%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1030-275-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.58 showing a Balanced dataset. Among the 4140 samples the target ground-truth class has changed 46 times representing a percentage of 1.12%. 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 0.172. to 3.494 and kurtosis values of 1.11 to 27.15. The fractal dimension analysis yields values ranging from -0.63 to -0.33 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.29, maximum 1.00, mean 0.59, and standard deviation 0.59. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 4.53%, average percentage of 0.91%, and standard deviation percentage of 2.03%.
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} |
1030-314-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.98 showing a Balanced dataset. Among the 4140 samples the target ground-truth class has changed 45 times representing a percentage of 1.09%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.169. to 9.908 and kurtosis values of 0.09 to 257.72. The fractal dimension analysis yields values ranging from -0.63 to -0.32 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.42, maximum 1.00, mean 0.53, and standard deviation 0.65. The count of numerical predictors with outliers is 5 with the minimum percentage of 0.17%, maximum percentage of 5.36%, average percentage of 1.42%, and standard deviation percentage of 2.25%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-52-2-1-3-classification.csv | A multivariate classification time-series dataset consists of 5358 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 5358 samples the target ground-truth class has changed 1147 times representing a percentage of 21.54%. 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 0.020. to 14.506 and kurtosis values of 0.00 to 277.91. The fractal dimension analysis yields values ranging from -0.68 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.96, maximum 1.00, mean 0.10, and standard deviation 0.48. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 9.94%, average percentage of 5.04%, and standard deviation percentage of 2.62%.
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} |
1031-39-2-1-6-classification.csv | A multivariate classification time-series dataset consists of 7227 samples and 15 features with 15 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.46 showing a Unbalanced dataset. Among the 7227 samples the target ground-truth class has changed 1503 times representing a percentage of 20.90%. There are 15 features in the dataset
Among the numerical predictors, the series has 13 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.026. to 0.914 and kurtosis values of 0.27 to 1.29. The fractal dimension analysis yields values ranging from -0.61 to -0.12 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.81, maximum 1.00, mean 0.13, and standard deviation 0.54. The count of numerical predictors with outliers is 14 with the minimum percentage of 0.00%, maximum percentage of 28.93%, average percentage of 6.16%, and standard deviation percentage of 7.12%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.01, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
3001-70.csv | A multivariate classification time-series dataset consists of 192 samples and 1 features with 1 numerical and 0 categorical features. Each instance has a window length of 4. The dataset has a sampling rate of 360.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00
The target column has 3 classes with entropy value 1.50 showing a Unbalanced dataset. Among the 192 samples the target ground-truth class has changed 177 times representing a percentage of 99.44%. There are 1 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 1 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 1 seasonality components detected in the numerical predictors. The top 1 common seasonality components are represented using sinusoidal waves. of periods 27 The numerical predictors also exhibit skewness values ranging from 0.858. to 0.858 and kurtosis values of 0.15 to 0.15. The fractal dimension analysis yields values ranging from -1.18 to -1.18 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 1.00, maximum 1.00, mean 1.00, and standard deviation 0.00. The count of numerical predictors with outliers is 0 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 40, 'n_estimators': 100} |
1031-41-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 6503 samples and 14 features with 12 numerical and 2 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.97 showing a Unbalanced dataset. Among the 6503 samples the target ground-truth class has changed 581 times representing a percentage of 8.98%. There are 14 features in the dataset with a ratio of numerical to categorical features of 6.0.
Among the numerical predictors, the series has 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 0.081. to 12.601 and kurtosis values of 0.04 to 195.77. The fractal dimension analysis yields values ranging from -0.83 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 0.15, and standard deviation 0.48. The count of numerical predictors with outliers is 10 with the minimum percentage of 0.00%, maximum percentage of 11.92%, average percentage of 1.89%, and standard deviation percentage of 3.37%.
Among the categorical predictors, the count of symbols ranges from 62 to 64 with a minimum entropy value 5.112843389393388, maximum entropy 5.349886843527727, mean 5.231365116460557, and standard deviation 0.1185217270671699,
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} |
1030-192-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.42 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 28 times representing a percentage of 0.68%. 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 1.316. to 4.048 and kurtosis values of 0.62 to 36.29. The fractal dimension analysis yields values ranging from -0.64 to -0.32 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.09, maximum 1.00, mean 0.65, and standard deviation 0.51. The count of numerical predictors with outliers is 5 with the minimum percentage of 5.04%, maximum percentage of 9.05%, average percentage of 8.15%, and standard deviation percentage of 1.74%.
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'} |
3001-64.csv | A multivariate classification time-series dataset consists of 24192 samples and 1 features with 1 numerical and 0 categorical features. Each instance has a window length of 6. The dataset has a sampling rate of 10.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 3 to 3 with mean 3.00
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 24192 samples the target ground-truth class has changed 1 times representing a percentage of 0.00%. There are 1 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 1 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.821. to 0.821 and kurtosis values of 0.87 to 0.87. The fractal dimension analysis yields values ranging from -0.95 to -0.95 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 1.00, maximum 1.00, mean 1.00, and standard deviation 0.00. The count of numerical predictors with outliers is 0 with the minimum percentage of 0.00%, maximum percentage of 0.00%, average percentage of 0.00%,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=1), 'learning_rate': 0.1, 'n_estimators': 50} |
1031-36-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 6980 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 6980 samples the target ground-truth class has changed 582 times representing a percentage of 8.38%. 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 0.015. to 3.134 and kurtosis values of 1.18 to 15.93. 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 -0.94, maximum 1.00, mean 0.12, and standard deviation 0.53. The count of numerical predictors with outliers is 15 with the minimum percentage of 32.65%, maximum percentage of 32.65%, average percentage of 32.65%, and standard deviation percentage of 0.00%.
Among the categorical predictors, the count of symbols ranges from 58 to 58 with a minimum entropy value 0.24852825088739017, maximum entropy 0.24852825088739017, mean 0.24852825088739017, 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} |
1031-18-1-1-3-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 1218 times representing a percentage of 16.54%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.032. to 10.910 and kurtosis values of 0.22 to 148.79. 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 -0.85, maximum 1.00, mean 0.14, and standard deviation 0.47. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 11.76%, average percentage of 5.69%, and standard deviation percentage of 3.10%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 50} |
1030-146-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 15 times representing a percentage of 0.36%. There are 5 features in the dataset
Among the numerical predictors, the series has 1 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.092. to 6.161 and kurtosis values of 0.79 to 98.03. The fractal dimension analysis yields values ranging from -0.64 to -0.31 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.59, maximum 1.00, mean 0.49, and standard deviation 0.74. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.26%, average percentage of 1.05%, and standard deviation percentage of 2.35%.
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 400} |
1031-44-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7562 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 7562 samples the target ground-truth class has changed 415 times representing a percentage of 5.51%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.627. to 3.080 and kurtosis values of 2.11 to 10.81. The fractal dimension analysis yields values ranging from -0.18 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 0.58, maximum 1.00, mean 0.93, and standard deviation 0.13. The count of numerical predictors with outliers is 16 with the minimum percentage of 23.10%, maximum percentage of 23.10%, average percentage of 23.10%, 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} |
1031-53-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 6716 samples and 7 features with 7 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 6716 samples the target ground-truth class has changed 573 times representing a percentage of 8.58%. There are 7 features in the dataset
Among the numerical predictors, the series has 7 numerical features detected as Stationary out of the 7 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 0.005. to 12.064 and kurtosis values of 0.15 to 198.91. The fractal dimension analysis yields values ranging from -0.72 to -0.09 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.40, maximum 1.00, mean 0.24, and standard deviation 0.58. The count of numerical predictors with outliers is 6 with the minimum percentage of 0.00%, maximum percentage of 9.13%, average percentage of 2.04%, and standard deviation percentage of 3.22%.
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} |
1031-18-1-1-6-classification.csv | A multivariate classification time-series dataset consists of 5938 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 5938 samples the target ground-truth class has changed 822 times representing a percentage of 13.92%. There are 16 features in the dataset with a ratio of numerical to categorical features of 15.0.
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.007. to 2.028 and kurtosis values of 0.53 to 7.80. The fractal dimension analysis yields values ranging from -0.61 to -0.13 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.85, maximum 1.00, mean 0.16, and standard deviation 0.50. The count of numerical predictors with outliers is 15 with the minimum percentage of 13.80%, maximum percentage of 45.82%, average percentage of 37.70%, and standard deviation percentage of 9.67%.
Among the categorical predictors, the count of symbols ranges from 61 to 61 with a minimum entropy value 0.322375481382463, maximum entropy 0.322375481382463, mean 0.322375481382463, 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} |
1031-16-2-1-6-classification.csv | A multivariate classification time-series dataset consists of 7459 samples and 16 features with 15 numerical and 1 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0
The target column has 3 classes with entropy value 1.44 showing a Unbalanced dataset. Among the 7459 samples the target ground-truth class has changed 1354 times representing a percentage of 18.24%. 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 0.013. to 1.667 and kurtosis values of 0.09 to 4.26. 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 -0.89, maximum 1.00, mean 0.12, and standard deviation 0.52. The count of numerical predictors with outliers is 15 with the minimum percentage of 1.08%, maximum percentage of 11.10%, average percentage of 6.59%, and standard deviation percentage of 3.65%.
Among the categorical predictors, the count of symbols ranges from 108 to 108 with a minimum entropy value 0.5019328866253744, maximum entropy 0.5019328866253744, mean 0.5019328866253744, 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} |
3001-29.csv | A multivariate classification time-series dataset consists of 648 samples and 2 features with 2 numerical and 0 categorical features. Each instance has a window length of 3. The dataset has a sampling rate of 480.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 2 classes with entropy value 0.99 showing a Balanced dataset. Among the 648 samples the target ground-truth class has changed 2 times representing a percentage of 0.31%. There are 2 features in the dataset
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 2 numerical features using the dickey-fuller test and the rest are Unstationary. 2 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 1.577. to 2.792 and kurtosis values of 1.49 to 9.32. The fractal dimension analysis yields values ranging from -1.03 to -1.02 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.48, maximum 1.00, mean 0.74, and standard deviation 0.26. The count of numerical predictors with outliers is 2 with the minimum percentage of 9.11%, maximum percentage of 9.11%, average percentage of 9.11%, and standard deviation percentage of 0.00%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=3), 'learning_rate': 0.1, 'n_estimators': 50} |
1031-43-2-1-2-classification.csv | A multivariate classification time-series dataset consists of 7455 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 1.44 showing a Unbalanced dataset. Among the 7455 samples the target ground-truth class has changed 1340 times representing a percentage of 18.06%. 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. 12 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.013. to 22.954 and kurtosis values of 0.02 to 879.17. 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 -0.95, maximum 1.00, mean 0.19, and standard deviation 0.51. The count of numerical predictors with outliers is 14 with the minimum percentage of 1.29%, maximum percentage of 14.43%, average percentage of 7.36%, and standard deviation percentage of 3.50%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-46-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7393 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.38 showing a Unbalanced dataset. Among the 7393 samples the target ground-truth class has changed 1328 times representing a percentage of 18.05%. There are 16 features in the dataset
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 15 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.010. to 15.523 and kurtosis values of 0.02 to 293.12. The fractal dimension analysis yields values ranging from -0.71 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.91, 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 3.17%, maximum percentage of 13.62%, average percentage of 9.34%, and standard deviation percentage of 3.23%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1031-6-1-1-2-classification.csv | A multivariate classification time-series dataset consists of 6684 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 6684 samples the target ground-truth class has changed 172 times representing a percentage of 2.59%. 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. 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 1.067. to 8.425 and kurtosis values of 6.42 to 75.36. The fractal dimension analysis yields values ranging from -0.23 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 0.35, maximum 1.00, mean 0.92, and standard deviation 0.20. The count of numerical predictors with outliers is 16 with the minimum percentage of 13.43%, maximum percentage of 13.43%, average percentage of 13.43%, 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} |
1031-29-1-1-1-classification.csv | A multivariate classification time-series dataset consists of 7449 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 7449 samples the target ground-truth class has changed 1418 times representing a percentage of 19.12%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.062. to 13.607 and kurtosis values of 0.02 to 237.57. 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 -0.96, maximum 1.00, mean 0.13, and standard deviation 0.50. The count of numerical predictors with outliers is 15 with the minimum percentage of 0.00%, maximum percentage of 10.28%, average percentage of 4.73%, and standard deviation percentage of 3.36%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1016-11-4-3-classification.csv | A multivariate classification time-series dataset consists of 7109 samples and 12 features with 5 numerical and 7 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00. Similarly, the missing values percentages for categorical features range from 0 to 0 with mean 0.0 and standard deviation 0.0.
The target column has 2 classes with entropy value 0.98 showing a Balanced dataset. Among the 7109 samples the target ground-truth class has changed 339 times representing a percentage of 4.79%. There are 12 features in the dataset with a ratio of numerical to categorical features of 0.7142857142857143.
Among the numerical predictors, the series has 5 numerical features detected as Stationary out of the 5 numerical features using the dickey-fuller test and the rest are Unstationary. 4 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 4 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 73,96,284 The numerical predictors also exhibit skewness values ranging from 0.081. to 1.085 and kurtosis values of 0.10 to 0.81. 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 -0.35, maximum 1.00, mean 0.36, and standard deviation 0.56. The count of numerical predictors with outliers is 3 with the minimum percentage of 0.00%, maximum percentage of 0.68%, average percentage of 0.28%, and standard deviation percentage of 0.29%.
Among the categorical predictors, the count of symbols ranges from 9 to 65 with a minimum entropy value 1.2955155378210943, maximum entropy 5.099058704194884, mean 3.448931347778909, and standard deviation 1.4348606931677317,
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
3001-50.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. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.390. to 0.390 and kurtosis values of 0.66 to 0.66. The fractal dimension analysis yields values ranging from -1.19 to -1.19 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 0.14%, maximum percentage of 0.14%, average percentage of 0.14%,
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} |
1030-302-classification.csv | A multivariate classification time-series dataset consists of 4140 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.58 showing a Balanced dataset. Among the 4140 samples the target ground-truth class has changed 28 times representing a percentage of 0.68%. 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 0.384. to 3.202 and kurtosis values of 1.17 to 18.21. The fractal dimension analysis yields values ranging from -0.59 to -0.31 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.31, maximum 1.00, mean 0.58, and standard deviation 0.61. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.02%, average percentage of 1.00%, 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'} |
1031-59-1-1-4-classification.csv | A multivariate classification time-series dataset consists of 7028 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 7028 samples the target ground-truth class has changed 1094 times representing a percentage of 15.64%. There are 16 features in the dataset with a ratio of numerical to categorical features of 15.0.
Among the numerical predictors, the series has 15 numerical features detected as Stationary out of the 15 numerical features using the dickey-fuller test and the rest are Unstationary. 11 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.002. to 2.859 and kurtosis values of 0.48 to 17.45. The fractal dimension analysis yields values ranging from -0.57 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 -0.98, maximum 1.00, mean 0.16, and standard deviation 0.55. The count of numerical predictors with outliers is 15 with the minimum percentage of 32.36%, maximum percentage of 47.61%, average percentage of 45.18%, and standard deviation percentage of 4.10%.
Among the categorical predictors, the count of symbols ranges from 62 to 62 with a minimum entropy value 0.2876567799693591, maximum entropy 0.2876567799693591, mean 0.2876567799693591, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 30} |
1031-9-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7846 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 7846 samples the target ground-truth class has changed 322 times representing a percentage of 4.12%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.586. to 62.341 and kurtosis values of 5.17 to 3891.44. The fractal dimension analysis yields values ranging from -0.79 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 -0.05, maximum 1.00, mean 0.83, and standard deviation 0.33. The count of numerical predictors with outliers is 16 with the minimum percentage of 14.20%, maximum percentage of 14.20%, average percentage of 14.20%, 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} |
1031-15-1-1-3-classification.csv | A multivariate classification time-series dataset consists of 7604 samples and 16 features with 16 numerical and 0 categorical features. Each instance has a window length of 24. The dataset has a sampling rate of 60.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.37 showing a Unbalanced dataset. Among the 7604 samples the target ground-truth class has changed 1312 times representing a percentage of 17.33%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.003. to 16.940 and kurtosis values of 0.24 to 363.67. The fractal dimension analysis yields values ranging from -0.60 to -0.11 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of -0.91, maximum 1.00, mean 0.08, and standard deviation 0.50. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.91%, maximum percentage of 32.38%, average percentage of 21.28%, and standard deviation percentage of 8.86%.
The dataset is converted into a simple classification task by extracting the previously described features. | XGBoostClassifier | {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 20, 'reg_lambda': 0.2} |
1016-4-1-2-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 303 times representing a percentage of 4.28%. 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 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.251. to 0.329 and kurtosis values of 0.07 to 0.65. The fractal dimension analysis yields values ranging from -0.50 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 -0.02, maximum 1.00, mean 0.61, and standard deviation 0.48. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.06%, maximum percentage of 0.64%, average percentage of 0.29%, and standard deviation percentage of 0.27%.
Among the categorical predictors, the count of symbols ranges from 9 to 67 with a minimum entropy value 1.2968201202630019, maximum entropy 5.205554875862875, mean 3.699536181287322, and standard deviation 1.4907850020707198,
The dataset is converted into a simple classification task by extracting the previously described features. | LassoClassifier | {'C': 9.517633316006078, 'penalty': 'l1', 'solver': 'saga'} |
1031-5-2-1-1-classification.csv | A multivariate classification time-series dataset consists of 7553 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 7553 samples the target ground-truth class has changed 1422 times representing a percentage of 18.91%. There are 16 features in the dataset
Among the numerical predictors, the series has 13 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 16 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 0.102. to 14.880 and kurtosis values of 0.01 to 269.16. 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 -0.85, maximum 1.00, mean 0.09, and standard deviation 0.52. The count of numerical predictors with outliers is 16 with the minimum percentage of 0.29%, maximum percentage of 16.04%, average percentage of 7.91%, and standard deviation percentage of 4.86%.
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} |
1030-221-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 1.85 showing a Unbalanced dataset. Among the 4140 samples the target ground-truth class has changed 29 times representing a percentage of 0.70%. 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 0.848. to 10.454 and kurtosis values of 0.52 to 218.09. 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 0.03, maximum 1.00, mean 0.69, and standard deviation 0.45. The count of numerical predictors with outliers is 1 with the minimum percentage of 0.00%, maximum percentage of 5.09%, average percentage of 1.02%, and standard deviation percentage of 2.28%.
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'} |
1016-5-2-2-classification.csv | A multivariate classification time-series dataset consists of 7125 samples and 8 features with 7 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 328 with mean 46.86 and standard deviation 123.97. Similarly, the missing values percentages for categorical features range from 328 to 328 with mean 328.0
The target column has 2 classes with entropy value 0.98 showing a Balanced dataset. Among the 7125 samples the target ground-truth class has changed 348 times representing a percentage of 5.15%. There are 8 features in the dataset with a ratio of numerical to categorical features of 7.0.
Among the numerical predictors, the series has 7 numerical features detected as Stationary out of the 7 numerical features using the dickey-fuller test and the rest are Unstationary. 7 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 11 seasonality components detected in the numerical predictors. The top 3 common seasonality components are represented using sinusoidal waves. of periods 219,251,849 The numerical predictors also exhibit skewness values ranging from 0.223. to 2.249 and kurtosis values of 0.23 to 5.13. The fractal dimension analysis yields values ranging from -0.51 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 -0.44, maximum 1.00, mean 0.27, and standard deviation 0.42. The count of numerical predictors with outliers is 4 with the minimum percentage of 0.00%, maximum percentage of 5.59%, average percentage of 1.21%, and standard deviation percentage of 2.06%.
Among the categorical predictors, the count of symbols ranges from 64 to 64 with a minimum entropy value 1.486249652951863, maximum entropy 1.486249652951863, mean 1.486249652951863, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 100.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
3001-27.csv | A multivariate classification time-series dataset consists of 792 samples and 1 features with 1 numerical and 0 categorical features. Each instance has a window length of 3. The dataset has a sampling rate of 480.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00
The target column has 2 classes with entropy value 1.00 showing a Balanced dataset. Among the 792 samples the target ground-truth class has changed 197 times representing a percentage of 25.16%. There are 1 features in the dataset
Among the numerical predictors, the series has 0 numerical features detected as Stationary out of the 1 numerical features using the dickey-fuller test and the rest are Unstationary. 1 of them are Multiplicative time-series features and the rest are Additive time-series features. There is an average of 0 seasonality components detected in the numerical predictors. The top 0 common seasonality components are represented using sinusoidal waves. The numerical predictors also exhibit skewness values ranging from 1.201. to 1.201 and kurtosis values of 0.44 to 0.44. The fractal dimension analysis yields values ranging from -0.58 to -0.58 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 1.00, maximum 1.00, mean 1.00, and standard deviation 0.00. The count of numerical predictors with outliers is 1 with the minimum percentage of 5.11%, maximum percentage of 5.11%, average percentage of 5.11%,
The dataset is converted into a simple classification task by extracting the previously described features. | RandomForestClassifier | {'max_depth': 10, 'n_estimators': 400} |
1030-91-classification.csv | A multivariate classification time-series dataset consists of 2805 samples and 5 features with 5 numerical and 0 categorical features. Each instance has a window length of 7. The dataset has a sampling rate of 1440.0 minutes. The dataset has a missing values percentage of 0.0%. The missing values percentages for numerical features range from 0 to 0 with mean 0.00 and standard deviation 0.00.
The target column has 3 classes with entropy value 1.54 showing a Unbalanced dataset. Among the 2805 samples the target ground-truth class has changed 26 times representing a percentage of 0.93%. 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 1.147. to 13.171 and kurtosis values of 0.04 to 279.98. The fractal dimension analysis yields values ranging from -0.59 to -0.32 indicating a Complex and Irregular time-series structure for the numerical predictors. The correlation values among the numerical predictors have a minimum of 0.09, maximum 1.00, mean 0.71, and standard deviation 0.42. The count of numerical predictors with outliers is 5 with the minimum percentage of 1.72%, maximum percentage of 5.41%, average percentage of 3.07%, and standard deviation percentage of 1.46%.
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'} |
1031-38-2-1-5-classification.csv | A multivariate classification time-series dataset consists of 6153 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 6153 samples the target ground-truth class has changed 854 times representing a percentage of 13.96%. There are 16 features in the dataset
Among the numerical predictors, the series has 16 numerical features detected as Stationary out of the 16 numerical features using the dickey-fuller test and the rest are Unstationary. 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 0.114. to 15.637 and kurtosis values of 0.30 to 333.36. 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 -0.55, maximum 1.00, mean 0.18, and standard deviation 0.47. The count of numerical predictors with outliers is 16 with the minimum percentage of 2.93%, maximum percentage of 45.48%, average percentage of 30.70%, and standard deviation percentage of 16.25%.
The dataset is converted into a simple classification task by extracting the previously described features. | AdaboostClassifier | {'estimator': DecisionTreeClassifier(max_depth=2), 'learning_rate': 1.0, 'n_estimators': 50} |
1031-27-2-1-4-classification.csv | A multivariate classification time-series dataset consists of 7886 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 1.37 showing a Unbalanced dataset. Among the 7886 samples the target ground-truth class has changed 937 times representing a percentage of 11.93%. 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 0.004. to 1.444 and kurtosis values of 1.35 to 6.95. The fractal dimension analysis yields values ranging from -0.39 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 0.20, and standard deviation 0.62. The count of numerical predictors with outliers is 12 with the minimum percentage of 41.98%, maximum percentage of 43.42%, average percentage of 43.30%, and standard deviation percentage of 0.42%.
Among the categorical predictors, the count of symbols ranges from 85 to 85 with a minimum entropy value 0.3179274316144422, maximum entropy 0.3179274316144422, mean 0.3179274316144422, and standard deviation 0.0,
The dataset is converted into a simple classification task by extracting the previously described features. | ElasticNetClassifier | {'C': 100.0, 'l1_ratio': 0.0001, 'penalty': 'elasticnet', 'solver': 'saga'} |
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