--- license: mit configs: - config_name: anomaly data_files: - split: train path: anomaly/train-* - config_name: autocorrelation_long_dependency data_files: - split: train path: autocorrelation_long_dependency/train-* - config_name: complex_multivariate_struct0 data_files: - split: train path: complex_multivariate_struct0/train-* - config_name: complex_multivariate_struct1 data_files: - split: train path: complex_multivariate_struct1/train-* - config_name: complex_multivariate_struct2 data_files: - split: train path: complex_multivariate_struct2/train-* - config_name: complex_multivariate_struct3 data_files: - split: train path: complex_multivariate_struct3/train-* - config_name: complex_multivariate_struct4 data_files: - split: train path: complex_multivariate_struct4/train-* - config_name: complex_multivariate_struct5 data_files: - split: train path: complex_multivariate_struct5/train-* - config_name: complex_multivariate_struct6 data_files: - split: train path: complex_multivariate_struct6/train-* - config_name: complex_univariate data_files: - split: train path: complex_univariate/train-* - config_name: cross_variable_learning_struct0 data_files: - split: train path: cross_variable_learning_struct0/train-* - config_name: cross_variable_learning_struct1 data_files: - split: train path: cross_variable_learning_struct1/train-* - config_name: cross_variable_learning_struct2 data_files: - split: train path: cross_variable_learning_struct2/train-* - config_name: cross_variable_learning_struct3 data_files: - split: train path: cross_variable_learning_struct3/train-* - config_name: cross_variable_learning_struct4 data_files: - split: train path: cross_variable_learning_struct4/train-* - config_name: datasetlength data_files: - split: train path: datasetlength/train-* - config_name: longdistance data_files: - split: train path: longdistance/train-* - config_name: noise data_files: - split: train path: noise/train-* - config_name: period data_files: - split: train path: period/train-* - config_name: trend data_files: - split: train path: trend/train-* task_categories: - time-series-forecasting dataset_info: - config_name: anomaly features: - name: date dtype: int64 - name: Feature1 dtype: float64 splits: - name: train num_bytes: 5760000 num_examples: 360000 download_size: 5378809 dataset_size: 5760000 - config_name: autocorrelation_long_dependency features: - name: date dtype: int64 - name: Feature1 dtype: float64 splits: - name: train num_bytes: 4000000 num_examples: 250000 download_size: 3735387 dataset_size: 4000000 - config_name: complex_multivariate_struct0 features: - name: date dtype: string - name: Feature1 dtype: float64 - name: Feature2 dtype: float64 splits: - name: train num_bytes: 150000 num_examples: 5000 download_size: 123368 dataset_size: 150000 - config_name: complex_multivariate_struct1 features: - name: date dtype: string - name: interest_rate dtype: float64 - name: inflation dtype: float64 - name: gdp_growth dtype: float64 - name: unemployment_rate dtype: float64 - name: consumer_spending dtype: float64 splits: - name: train num_bytes: 270000 num_examples: 5000 download_size: 266065 dataset_size: 270000 - config_name: complex_multivariate_struct2 features: - name: date dtype: string - name: temperature dtype: float64 - name: rainfall dtype: float64 - name: ice_cream_sales dtype: float64 - name: umbrella_sales dtype: float64 - name: beverage_sales dtype: float64 splits: - name: train num_bytes: 270000 num_examples: 5000 download_size: 258007 dataset_size: 270000 - config_name: complex_multivariate_struct3 features: - name: date dtype: string - name: Feature1 dtype: float64 - name: Feature2 dtype: float64 - name: Feature3 dtype: float64 splits: - name: train num_bytes: 190000 num_examples: 5000 download_size: 165048 dataset_size: 190000 - config_name: complex_multivariate_struct4 features: - name: date dtype: string - name: economic_growth dtype: float64 - name: employment_rate dtype: float64 - name: market_confidence dtype: float64 - name: negative_indicator dtype: float64 splits: - name: train num_bytes: 230000 num_examples: 5000 download_size: 218653 dataset_size: 230000 - config_name: complex_multivariate_struct5 features: - name: date dtype: string - name: ad_spend dtype: float64 - name: sales dtype: float64 splits: - name: train num_bytes: 150000 num_examples: 5000 download_size: 123335 dataset_size: 150000 - config_name: complex_multivariate_struct6 features: - name: date dtype: string - name: supply dtype: float64 - name: demand dtype: float64 - name: price dtype: float64 splits: - name: train num_bytes: 190000 num_examples: 5000 download_size: 145033 dataset_size: 190000 - config_name: complex_univariate features: - name: date dtype: int64 - name: Feature1 dtype: float64 splits: - name: train num_bytes: 800000 num_examples: 50000 download_size: 747380 dataset_size: 800000 - config_name: cross_variable_learning_struct0 features: - name: date dtype: int64 - name: FeatureA_WhiteNoise dtype: float64 - name: FeatureB_Lag48 dtype: float64 splits: - name: train num_bytes: 120000 num_examples: 5000 download_size: 122635 dataset_size: 120000 - config_name: cross_variable_learning_struct1 features: - name: date dtype: int64 - name: FeatureA_WhiteNoise dtype: float64 - name: FeatureB_Lag10 dtype: float64 splits: - name: train num_bytes: 120000 num_examples: 5000 download_size: 122989 dataset_size: 120000 - config_name: cross_variable_learning_struct2 features: - name: date dtype: int64 - name: FeatureA_Sin dtype: float64 - name: FeatureB_Noise_0dB dtype: float64 - name: FeatureC_Sum dtype: float64 splits: - name: train num_bytes: 160000 num_examples: 5000 download_size: 150800 dataset_size: 160000 - config_name: cross_variable_learning_struct3 features: - name: date dtype: int64 - name: FeatureA_WhiteNoise dtype: float64 - name: FeatureB_Lag5 dtype: float64 splits: - name: train num_bytes: 120000 num_examples: 5000 download_size: 123027 dataset_size: 120000 - config_name: cross_variable_learning_struct4 features: - name: date dtype: int64 - name: FeatureA_WhiteNoise dtype: float64 - name: FeatureB_Lag24 dtype: float64 splits: - name: train num_bytes: 120000 num_examples: 5000 download_size: 122857 dataset_size: 120000 - config_name: datasetlength features: - name: date dtype: int64 - name: Feature1 dtype: float64 splits: - name: train num_bytes: 33024000 num_examples: 2064000 download_size: 30150682 dataset_size: 33024000 - config_name: longdistance features: - name: date dtype: int64 - name: Feature1 dtype: float64 splits: - name: train num_bytes: 240000 num_examples: 15000 download_size: 127490 dataset_size: 240000 - config_name: noise features: - name: date dtype: int64 - name: Feature1 dtype: float64 splits: - name: train num_bytes: 3840000 num_examples: 240000 download_size: 3509672 dataset_size: 3840000 - config_name: period features: - name: date dtype: int64 - name: Feature1 dtype: float64 splits: - name: train num_bytes: 800000 num_examples: 50000 download_size: 514654 dataset_size: 800000 - config_name: trend features: - name: date dtype: int64 - name: Feature1 dtype: float64 splits: - name: train num_bytes: 880000 num_examples: 55000 download_size: 742881 dataset_size: 880000 --- # SynTSBench: A Synthetic Time Series Benchmark SynTSBench is a comprehensive synthetic time series benchmark dataset designed for evaluating machine learning models on various time series tasks. ## Dataset Structure The dataset is organized into distinct configurations based on different time series characteristics and column structures: - **anomaly**: Synthetic time series data from Dataset_generated_anomaly with columns: Feature1, date - **autocorrelation_long_dependency**: Synthetic time series data from Dataset_generated_autocorrelation-long-dependency with columns: Feature1, date - **complex_multivariate_struct0**: Synthetic time series data from Dataset_generated_complex_multivariate with columns: Feature1, Feature2, date - **complex_multivariate_struct1**: Synthetic time series data from Dataset_generated_complex_multivariate with columns: consumer_spending, date, gdp_growth, inflation, interest_rate, unemployment_rate - **complex_multivariate_struct2**: Synthetic time series data from Dataset_generated_complex_multivariate with columns: beverage_sales, date, ice_cream_sales, rainfall, temperature, umbrella_sales - **complex_multivariate_struct3**: Synthetic time series data from Dataset_generated_complex_multivariate with columns: Feature1, Feature2, Feature3, date - **complex_multivariate_struct4**: Synthetic time series data from Dataset_generated_complex_multivariate with columns: date, economic_growth, employment_rate, market_confidence, negative_indicator - **complex_multivariate_struct5**: Synthetic time series data from Dataset_generated_complex_multivariate with columns: ad_spend, date, sales - **complex_multivariate_struct6**: Synthetic time series data from Dataset_generated_complex_multivariate with columns: date, demand, price, supply - **complex_univariate**: Synthetic time series data from Dataset_generated_complex_univariate with columns: Feature1, date - **cross_variable_learning_struct0**: Synthetic time series data from Dataset_generated_cross-variable-learning with columns: FeatureA_WhiteNoise, FeatureB_Lag48, date - **cross_variable_learning_struct1**: Synthetic time series data from Dataset_generated_cross-variable-learning with columns: FeatureA_WhiteNoise, FeatureB_Lag10, date - **cross_variable_learning_struct2**: Synthetic time series data from Dataset_generated_cross-variable-learning with columns: FeatureA_Sin, FeatureB_Noise_0dB, FeatureC_Sum, date - **cross_variable_learning_struct3**: Synthetic time series data from Dataset_generated_cross-variable-learning with columns: FeatureA_WhiteNoise, FeatureB_Lag5, date - **cross_variable_learning_struct4**: Synthetic time series data from Dataset_generated_cross-variable-learning with columns: FeatureA_WhiteNoise, FeatureB_Lag24, date - **datasetlength**: Synthetic time series data from Dataset_generated_datasetlength with columns: Feature1, date - **longdistance**: Synthetic time series data from Dataset_generated_longdistance with columns: Feature1, date - **noise**: Synthetic time series data from Dataset_generated_noise with columns: Feature1, date - **period**: Synthetic time series data from Dataset_generated_period with columns: Feature1, date - **trend**: Synthetic time series data from Dataset_generated_trend with columns: Feature1, date