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13
64
start
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375 values
end
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443 values
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6 values
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288
7.4M
Health_SelfRegulationSCP1_458_0
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Health_SelfRegulationSCP1_458_1
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Health_SelfRegulationSCP1_458_2
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Health_SelfRegulationSCP1_458_3
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Health_SelfRegulationSCP1_458_4
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Health_SelfRegulationSCP1_458_5
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Health_SelfRegulationSCP1_219_0
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Health_SelfRegulationSCP1_219_1
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Health_SelfRegulationSCP1_219_2
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Health_SelfRegulationSCP1_219_3
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Health_SelfRegulationSCP1_219_4
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Health_SelfRegulationSCP1_219_5
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Unified Time Series Dataset (UTSD)

Updates

:triangular_flag_on_post: News (2024.10) We release the numpy format of UTSD. An easier and more efficient dataloader can be found here.

Introduction

We curate Unified Time Series Dataset (UTSD) that includes 7 domains with up to 1 billion time points with hierarchical four volumes to facilitate research of large models and pre-training in the field of time series.

Unified Time Series Dataset (UTSD) is meticulously assembled from a blend of publicly accessible online data repositories and empirical data derived from real-world machine operations.

All datasets are classified into seven distinct domains by their source: Energy, Environment, Health, Internet of Things (IoT), Nature, Transportation, and Web with diverse sampling frequencies.

See the paper and codebase for more information.

Dataset detailed descriptions.

We analyze each dataset, examining the time series through the lenses of stationarity and forecastability to allow us to characterize the level of complexity inherent to each dataset.

Domain Dataset Time Points File Size Freq. ADF. Forecast. Source
Energy London Smart Meters 166.50M 4120M Hourly -13.158 0.173 [1]
Energy Wind Farms 7.40M 179M 4 sec -29.174 0.811 [1]
Energy Aus. Electricity Demand 1.16M 35M 30 min -27.554 0.730 [1]
Environment AustraliaRainfall 11.54M 54M Hourly -150.10 0.458 [2]
Environment BeijingPM25Quality 3.66M 26M Hourly -31.415 0.404 [2]
Environment BenzeneConcentration 16.34M 206M Hourly -65.187 0.526 [2]
Health MotorImagery 72.58M 514M 0.001 sec -3.132 0.449 [3]
Health SelfRegulationSCP1 3.02M 18M 0.004 sec -3.191 0.504 [3]
Health SelfRegulationSCP2 3.06M 18M 0.004 sec -2.715 0.481 [3]
Health AtrialFibrillation 0.04M 1M 0.008 sec -7.061 0.167 [3]
Health PigArtPressure 0.62M 7M - -7.649 0.739 [3]
Health PigCVP 0.62M 7M - -4.855 0.577 [3]
Health IEEEPPG 15.48M 136M 0.008 sec -7.725 0.380 [2]
Health BIDMC32HR 63.59M 651M - -14.135 0.523 [2]
Health TDBrain 72.30M 1333M 0.002 sec -3.167 0.967 [5]
IoT SensorData 165.4M 2067M 0.02 sec -15.892 0.917 Real-world machine logs
Nature Phoneme 2.16M 25M - -8.506 0.243 [3]
Nature EigenWorms 27.95M 252M - -12.201 0.393 [3]
Nature ERA5 Surface 58.44M 574M 3 h -28.263 0.493 [4]
Nature ERA5 Pressure 116.88M 1083M 3h -22.001 0.853 [4]
Nature Temperature Rain 23.25M 109M Daily -10.952 0.133 [1]
Nature StarLightCurves 9.46M 109M - -1.891 0.555 [3]
Nature Saugen River Flow 0.02M 1M Daily -19.305 0.300 [1]
Nature KDD Cup 2018 2.94M 67M Hourly -10.107 0.362 [1]
Nature US Births 0.00M 1M Daily -3.352 0.675 [1]
Nature Sunspot 0.07M 2M Daily -7.866 0.287 [1]
Nature Worms 0.23M 4M 0.033 sec -3.851 0.395 [3]
Transport Pedestrian Counts 3.13M 72M Hourly -23.462 0.297 [1]
Web Web Traffic 116.49M 388M Daily -8.272 0.299 [1]

You can find the specific source address in source.csv.

[1]: Monash Time Series Forecasting Archive [2]: Time series extrinsic regression Predicting numeric values from time series data [3]: The UCR Time Series Archive [4]: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications [5]: Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series

Hierarchy of Datasets

UTSD is constructed with hierarchical capacities, namely UTSD-1G, UTSD-2G, UTSD-4G, and UTSD-12G, where each smaller dataset is a subset of the larger ones. A larger subset means greater data difficulty and diversity, allowing you to conduct detailed scaling experiments.

Usage

You can access and load UTSD based on the code in our repo.

# huggingface-cli login
# export HF_ENDPOINT=https://hf-mirror.com 

python ./scripts/UTSD/download_dataset.py

# dataloader
python ./scripts/UTSD/utsdataset.py

It should be noted that due to the construction of our dataset with diverse lengths, the sequence lengths of different samples vary. You can construct the data organization logic according to your own needs.

In addition, we provide code dataset_evaluation.py for evaluating time series datasets, which you can use to evaluate your Huggingface formatted dataset. The usage of this script is as follows:

python dataset_evaluation.py --root_path <dataset root path> --log_path <output log path>

Acknowledgments

UTSD is mostly built from the Internet public time series dataset, which comes from different research teams and providers. We sincerely thank all individuals and organizations who have contributed the data. Without their generous sharing, this dataset would not have existed.

Citation

If you're using UTSD in your research or applications, please cite it using this BibTeX:

BibTeX:

@inproceedings{liutimer,
  title={Timer: Generative Pre-trained Transformers Are Large Time Series Models},
  author={Liu, Yong and Zhang, Haoran and Li, Chenyu and Huang, Xiangdong and Wang, Jianmin and Long, Mingsheng},
  booktitle={Forty-first International Conference on Machine Learning}
}
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