Awesome Self-Supervised Learning for Time Series (SSL4TS)

A professionally curated list of awesome resources (paper, code, data, etc.) on Self-Supervised Learning for Time Series (SSL4TS), which is the first work to comprehensively and systematically summarize the recent advances of Self-Supervised Learning for modeling time series data to the best of our knowledge.

We will continue to update this list with the newest resources. If you find any missed resources (paper/code) or errors, please feel free to open an issue or make a pull request.

For general AI for Time Series (AI4TS) Papers, Tutorials, and Surveys at the Top AI Conferences and Journals, please check This Repo.

Survey paper

Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects

Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, Shirui Pan.

If you find this repository helpful for your work, please kindly cite our survey paper.

@article{zhang2023ssl4ts,
  title={Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects},
  author={Kexin Zhang and Qingsong Wen and Chaoli Zhang and Rongyao Cai and Ming Jin and Yong Liu and James Zhang and Yuxuan Liang and Guansong Pang and Dongjin Song and Shirui Pan}
  journal={arXiv preprint arXiv:2306.10125},
  year={2023}
}

Taxonomy of Self-Supervised Learning for Time Series

Category of Self-Supervised Learning for Time Series

Generative-based Methods on SSL4TS

Autoregressive-based forecasting

Autoencoder-based reconstruction

Diffusion-based generation

Contrastive-based Methods on SSL4TS

Sampling contrast

Prediction contrast

Augmentation contrast

Prototype contrast

Expert knowledge contrast

Adversarial-based Methods on SSL4TS

Time series generation and imputation

Auxiliary representation enhancement

Applications and Datasets on SSL4TS

Forecasting

Anomaly Detection

Classification and Clustering

Time Series Related Survey

Transformers in Time Series: A Survey, in IJCAI 2023. [paper] [link]
Time series data augmentation for deep learning: a survey, in IJCAI 2021. [paper]
Neural temporal point processes: a review, in IJCAI 2021. [paper]
Time-series forecasting with deep learning: a survey, in Philosophical Transactions of the Royal Society A 2021. [paper]
Deep learning for time series forecasting: a survey, in Big Data 2021. [paper]
Neural forecasting: Introduction and literature overview, in arXiv 2020. [paper]
Deep learning for anomaly detection in time-series data: review, analysis, and guidelines, in Access 2021. [paper]
A review on outlier/anomaly detection in time series data, in ACM Computing Surveys 2021. [paper]
A unifying review of deep and shallow anomaly detection, in Proceedings of the IEEE 2021. [paper]
Deep learning for time series classification: a review, in Data Mining and Knowledge Discovery 2019. [paper]
More related time series surveys, tutorials, and papers can be found at this repo.

Self-Supervised Learning Tutorial/Survey in Other Disciplines

A cookbook of self-supervised learning, in arXiv 2023. [paper]
Self-supervised Learning: Generative or Contrastive, in TKDE 2021. [paper]

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awesome-self-supervised-learning-timeseries's Introduction