Datasets:

lalababa
/

Time-Series-Library

tags:
  - time-series
  - forecasting
  - anomaly-detection
  - classification
  - TSLib
license: cc-by-4.0
task_categories:
  - time-series-forecasting
pretty_name: Time-Series-Library (TSLib)
language:
  - en
size_categories:
  - 100B<n<1T
configs:
  - config_name: ETTh1
    description: ETT long-term forecasting subset ETTh1 (hourly).
    data_files:
      - ETT-small/ETTh1.csv
  - config_name: ETTh2
    description: ETT long-term forecasting subset ETTh2 (hourly).
    data_files:
      - ETT-small/ETTh2.csv
  - config_name: ETTm1
    description: ETT long-term forecasting subset ETTm1 (15-min).
    data_files:
      - ETT-small/ETTm1.csv
  - config_name: ETTm2
    description: ETT long-term forecasting subset ETTm2 (15-min).
    data_files:
      - ETT-small/ETTm2.csv
  - config_name: electricity
    description: Electricity load forecasting (UCI Electricity).
    data_files:
      - electricity/electricity.csv
  - config_name: traffic
    description: Traffic volume forecasting.
    data_files:
      - traffic/traffic.csv
  - config_name: weather
    description: Weather time-series forecasting.
    data_files:
      - weather/weather.csv
  - config_name: exchange_rate
    description: Exchange rate forecasting.
    data_files:
      - exchange_rate/exchange_rate.csv
  - config_name: national_illness
    description: Influenza-like illness (ILI) forecasting.
    data_files:
      - illness/national_illness.csv
  - config_name: m4-yearly
    description: M4 Yearly forecasting subset.
    data_files:
      - m4/Yearly-train.csv
      - m4/Yearly-test.csv
  - config_name: m4-quarterly
    description: M4 Quarterly forecasting subset.
    data_files:
      - m4/Quarterly-train.csv
      - m4/Quarterly-test.csv
  - config_name: m4-monthly
    description: M4 Monthly forecasting subset.
    data_files:
      - m4/Monthly-train.csv
      - m4/Monthly-test.csv
  - config_name: m4-weekly
    description: M4 Weekly forecasting subset.
    data_files:
      - m4/Weekly-train.csv
      - m4/Weekly-test.csv
  - config_name: m4-daily
    description: M4 Daily forecasting subset.
    data_files:
      - m4/Daily-train.csv
      - m4/Daily-test.csv
  - config_name: m4-hourly
    description: M4 Hourly forecasting subset.
    data_files:
      - m4/Hourly-train.csv
      - m4/Hourly-test.csv
  - config_name: EthanolConcentration
    description: 'UEA multivariate classification: EthanolConcentration.'
    data_files:
      - EthanolConcentration/EthanolConcentration_TRAIN.ts
      - EthanolConcentration/EthanolConcentration_TEST.ts
  - config_name: FaceDetection
    description: 'UEA multivariate classification: FaceDetection.'
    data_files:
      - FaceDetection/FaceDetection_TRAIN.ts
      - FaceDetection/FaceDetection_TEST.ts
  - config_name: Handwriting
    description: 'UEA multivariate classification: Handwriting.'
    data_files:
      - Handwriting/Handwriting_TRAIN.ts
      - Handwriting/Handwriting_TEST.ts
  - config_name: Heartbeat
    description: 'UEA multivariate classification: Heartbeat.'
    data_files:
      - Heartbeat/Heartbeat_TRAIN.ts
      - Heartbeat/Heartbeat_TEST.ts
  - config_name: JapaneseVowels
    description: 'UEA multivariate classification: JapaneseVowels.'
    data_files:
      - JapaneseVowels/JapaneseVowels_TRAIN.ts
      - JapaneseVowels/JapaneseVowels_TEST.ts
  - config_name: PEMS-SF
    description: 'UEA multivariate classification: PEMS-SF.'
    data_files:
      - PEMS-SF/PEMS-SF_TRAIN.ts
      - PEMS-SF/PEMS-SF_TEST.ts
  - config_name: SelfRegulationSCP1
    description: 'UEA multivariate classification: SelfRegulationSCP1.'
    data_files:
      - SelfRegulationSCP1/SelfRegulationSCP1_TRAIN.ts
      - SelfRegulationSCP1/SelfRegulationSCP1_TEST.ts
  - config_name: SelfRegulationSCP2
    description: 'UEA multivariate classification: SelfRegulationSCP2.'
    data_files:
      - SelfRegulationSCP2/SelfRegulationSCP2_TRAIN.ts
      - SelfRegulationSCP2/SelfRegulationSCP2_TEST.ts
  - config_name: SpokenArabicDigits
    description: 'UEA multivariate classification: SpokenArabicDigits.'
    data_files:
      - SpokenArabicDigits/SpokenArabicDigits_TRAIN.ts
      - SpokenArabicDigits/SpokenArabicDigits_TEST.ts
  - config_name: UWaveGestureLibrary
    description: 'UEA multivariate classification: UWaveGestureLibrary.'
    data_files:
      - UWaveGestureLibrary/UWaveGestureLibrary_TRAIN.ts
      - UWaveGestureLibrary/UWaveGestureLibrary_TEST.ts
  - config_name: SMD
    description: Server Machine Dataset (SMD) for anomaly detection.
    data_files:
      - SMD/SMD_train.npy
      - SMD/SMD_test.npy
      - SMD/SMD_test_label.npy
      - SMD/SMD_train.pkl
      - SMD/SMD_test.pkl
      - SMD/SMD_test_label.pkl
  - config_name: MSL
    description: NASA Mars Science Laboratory (MSL) anomaly detection.
    data_files:
      - MSL/MSL_train.npy
      - MSL/MSL_test.npy
      - MSL/MSL_test_label.npy
  - config_name: SMAP
    description: NASA Soil Moisture Active Passive (SMAP) anomaly detection.
    data_files:
      - SMAP/SMAP_train.npy
      - SMAP/SMAP_test.npy
      - SMAP/SMAP_test_label.npy
  - config_name: PSM
    description: KPI-based Process/System Monitoring anomaly detection.
    data_files:
      - PSM/train.csv
      - PSM/test.csv
      - PSM/test_label.csv
  - config_name: SWaT
    description: >-
      Secure Water Treatment (SWaT) anomaly detection; CSV are preprocessed,
      Excel are raw.
    data_files:
      - SWaT/swat_train.csv
      - SWaT/swat_train2.csv
      - SWaT/swat2.csv
      - SWaT/swat_raw.csv
      - SWaT/SWaT_Dataset_Normal_v1.xlsx
      - SWaT/SWaT_Dataset_Attack_v0.xlsx

Time-Series-Library (TSLib)

TSLib is an open-source library for deep learning researchers, especially for deep time series analysis.

We provide a neat code base to evaluate advanced deep time series models or develop your model, which covers five mainstream tasks: long- and short-term forecasting, imputation, anomaly detection, and classification.

This benchmark collection is designed to evaluate and develop advanced deep time-series models. For an in-depth exploration of current time-series models and their performance, please refer to our paper Deep Time Series Models: A Comprehensive Survey and Benchmark.

To get started with the codebase and contribute, please visit the GitHub repository.

Dataset Overview

Tasks	Benchmarks	Metrics	Series Length
Forecasting	Long-term: ETT (4 subsets), Electricity, Traffic, Weather, Exchange, ILI	MSE, MAE	96~720 (ILI: 24~60)
	Short-term: M4 (6 subsets)	SMAPE, MASE, OWA	6~48
Imputation	ETT (4 subsets), Electricity, Weather	MSE, MAE	96
Classification	UEA (10 subsets)	Accuracy	29~1751
Anomaly Detection	SMD, MSL, SMAP, SWaT, PSM	Precision, Recall, F1-Score	100

File Structure

Time-Series-Library/
├── ETT-small/
├── EthanolConcentration/
├── FaceDetection/
├── Handwriting/
├── Heartbeat/
├── JapaneseVowels/
├── MSL/
├── PEMS-SF/
├── PSM/
├── SMAP/
├── SMD/
├── SWaT/
├── SelfRegulationSCP1/
├── SelfRegulationSCP2/
├── SpokenArabicDigits/
├── UWaveGestureLibrary/
├── electricity/
├── exchange_rate/
├── illness/
├── m4/
├── traffic/
├── weather/
└── .gitattributes

Usage

You can load the dataset directly using the datasets library:

from datasets import load_dataset
dataset = load_dataset("lalababa/Time-Series-Library", "ETT-small")

License

This dataset is released under the CC BY 4.0 License.

Citation

If you find this repo useful, please cite our paper.

@inproceedings{wu2023timesnet,
  title={TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis},
  author={Haixu Wu and Tengge Hu and Yong Liu and Hang Zhou and Jianmin Wang and Mingsheng Long},
  booktitle={International Conference on Learning Representations},
  year={2023},
}

@article{wang2024tssurvey,
  title={Deep Time Series Models: A Comprehensive Survey and Benchmark},
  author={Yuxuan Wang and Haixu Wu and Jiaxiang Dong and Yong Liu and Mingsheng Long and Jianmin Wang},
  booktitle={arXiv preprint arXiv:2407.13278},
  year={2024},
}