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Time-Series-Library

Tasks:
Time Series Forecasting
Modalities:
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Languages:
English
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1M - 10M
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Tags:
time-series
forecasting
anomaly-detection
classification
TSLib
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The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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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
└── README.md

Usage

You can load the dataset directly using the datasets library:

from datasets import load_dataset
dataset = load_dataset("thuml/Time-Series-Library", "ETTh1")

Or download specific files with hf_hub_download:

from huggingface_hub import hf_hub_download
hf_hub_download("thuml/Time-Series-Library", "ETT-small/ETTh1.csv", repo_type="dataset")

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},
}
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