Datasets:
metadata
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