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@article{cheng2023timemae,
  title={Timemae: Self-supervised representations of time series with decoupled masked autoencoders},
  author={Cheng, Mingyue and Liu, Qi and Liu, Zhiding and Zhang, Hao and Zhang, Rujiao and Chen, Enhong},
  journal={arXiv preprint arXiv:2303.00320},
  year={2023}
}

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Introduction

In this work, we propose TimeMAE, a novel self-supervised paradigm for learning transferrable time series representations based on transformer networks. The distinct characteristics of the TimeMAE lie in processing each time series into a sequence of non-overlapping sub-series via window-slicing partitioning, followed by random masking strategies over the semantic units of localized sub-series. Such a simple yet effective setting can help us achieve the goal of killing three birds with one stone, i.e., (1) learning enriched contextual representations of time series with a bidirectional encoding scheme; (2) increasing the information density of basic semantic units; (3) efficiently encoding representations of time series using transformer networks. Nevertheless, it is a non-trivial to perform reconstructing task over such a novel formulated modeling paradigm. To solve the discrepancy issue incurred by newly injected masked embeddings, we design a decoupled autoencoder architecture, which learns the representations of visible (unmasked) positions and masked ones with two different encoder modules, respectively. Furthermore, we construct two types of informative targets to accomplish the corresponding pretext tasks. One is to create a tokenizer module that assigns a codeword to each masked region, allowing the masked codeword classification (MCC) task to be completed effectively. Another one is to adopt a siamese network structure to generate target representations for each masked input unit, aiming at performing the masked representation regression (MRR) optimization. Comprehensively pre-trained, our model can efficiently learn transferrable time series representations, thus benefiting the classification of time series

Further Reading

1, CrossTimeNet: Learning Transferable Time Series Classifier with Cross-Domain Pre-training from Language Model.

Authors: Cheng, Mingyue and Tao, Xiaoyu and Liu, Qi and Zhang, Hao and Chen, Yiheng and Lei, Chenyi

@article{cheng2024learning,
  title={Learning Transferable Time Series Classifier with Cross-Domain Pre-training from Language Model},
  author={Cheng, Mingyue and Tao, Xiaoyu and Liu, Qi and Zhang, Hao and Chen, Yiheng and Lei, Chenyi},
  journal={arXiv preprint arXiv:2403.12372},
  year={2024}
}

2, FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification.

Authors: Cheng, Mingyue and Liu, Qi and Liu, Zhiding and Li, Zhi and Luo, Yucong and Chen, Enhong

@inproceedings{cheng2023formertime,
  title={Formertime: Hierarchical multi-scale representations for multivariate time series classification},
  author={Cheng, Mingyue and Liu, Qi and Liu, Zhiding and Li, Zhi and Luo, Yucong and Chen, Enhong},
  booktitle={Proceedings of the ACM Web Conference 2023},
  pages={1437--1445},
  year={2023}
}

3, Convtimenet: A deep hierarchical fully convolutional model for multivariate time series analysis.

Authors: Cheng, Mingyue and Yang, Jiqian and Pan, Tingyue and Liu, Qi and Li, Zhi

@article{cheng2024convtimenet,
  title={Convtimenet: A deep hierarchical fully convolutional model for multivariate time series analysis},
  author={Cheng, Mingyue and Yang, Jiqian and Pan, Tingyue and Liu, Qi and Li, Zhi},
  journal={arXiv preprint arXiv:2403.01493},
  year={2024}
}

4, InstructTime: Advancing Time Series Classification with Multimodal Language Modeling.

Authors: Cheng, Mingyue and Chen, Yiheng and Liu, Qi and Liu, Zhiding and Luo, Yucong

@article{cheng2024advancing,
  title={Advancing Time Series Classification with Multimodal Language Modeling},
  author={Cheng, Mingyue and Chen, Yiheng and Liu, Qi and Liu, Zhiding and Luo, Yucong},
  journal={arXiv preprint arXiv:2403.12371},
  year={2024}
}