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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 8: AAAI-22 Technical Tracks 8

TS2Vec: Towards Universal Representation of Time Series

February 1, 2023

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Authors

Zhihan Yue

Peking University Microsoft


Yujing Wang

Microsoft Peking University


Juanyong Duan

Microsoft


Tianmeng Yang

Peking University Microsoft


Congrui Huang

Microsoft


Yunhai Tong

Peking University


Bixiong Xu

Microsoft


DOI:

10.1609/aaai.v36i8.20881


Abstract:

This paper presents TS2Vec, a universal framework for learning representations of time series in an arbitrary semantic level. Unlike existing methods, TS2Vec performs contrastive learning in a hierarchical way over augmented context views, which enables a robust contextual representation for each timestamp. Furthermore, to obtain the representation of an arbitrary sub-sequence in the time series, we can apply a simple aggregation over the representations of corresponding timestamps. We conduct extensive experiments on time series classification tasks to evaluate the quality of time series representations. As a result, TS2Vec achieves significant improvement over existing SOTAs of unsupervised time series representation on 125 UCR datasets and 29 UEA datasets. The learned timestamp-level representations also achieve superior results in time series forecasting and anomaly detection tasks. A linear regression trained on top of the learned representations outperforms previous SOTAs of time series forecasting. Furthermore, we present a simple way to apply the learned representations for unsupervised anomaly detection, which establishes SOTA results in the literature. The source code is publicly available at https://github.com/yuezhihan/ts2vec.

Topics: AAAI

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HOW TO CITE:

Zhihan Yue||Yujing Wang||Juanyong Duan||Tianmeng Yang||Congrui Huang||Yunhai Tong||Bixiong Xu TS2Vec: Towards Universal Representation of Time Series Proceedings of the AAAI Conference on Artificial Intelligence (2022) 8980-8987.

Zhihan Yue||Yujing Wang||Juanyong Duan||Tianmeng Yang||Congrui Huang||Yunhai Tong||Bixiong Xu TS2Vec: Towards Universal Representation of Time Series AAAI 2022, 8980-8987.

Zhihan Yue||Yujing Wang||Juanyong Duan||Tianmeng Yang||Congrui Huang||Yunhai Tong||Bixiong Xu (2022). TS2Vec: Towards Universal Representation of Time Series. Proceedings of the AAAI Conference on Artificial Intelligence, 8980-8987.

Zhihan Yue||Yujing Wang||Juanyong Duan||Tianmeng Yang||Congrui Huang||Yunhai Tong||Bixiong Xu. TS2Vec: Towards Universal Representation of Time Series. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.8980-8987.

Zhihan Yue||Yujing Wang||Juanyong Duan||Tianmeng Yang||Congrui Huang||Yunhai Tong||Bixiong Xu. 2022. TS2Vec: Towards Universal Representation of Time Series. "Proceedings of the AAAI Conference on Artificial Intelligence". 8980-8987.

Zhihan Yue||Yujing Wang||Juanyong Duan||Tianmeng Yang||Congrui Huang||Yunhai Tong||Bixiong Xu. (2022) "TS2Vec: Towards Universal Representation of Time Series", Proceedings of the AAAI Conference on Artificial Intelligence, p.8980-8987

Zhihan Yue||Yujing Wang||Juanyong Duan||Tianmeng Yang||Congrui Huang||Yunhai Tong||Bixiong Xu, "TS2Vec: Towards Universal Representation of Time Series", AAAI, p.8980-8987, 2022.

Zhihan Yue||Yujing Wang||Juanyong Duan||Tianmeng Yang||Congrui Huang||Yunhai Tong||Bixiong Xu. "TS2Vec: Towards Universal Representation of Time Series". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.8980-8987.

Zhihan Yue||Yujing Wang||Juanyong Duan||Tianmeng Yang||Congrui Huang||Yunhai Tong||Bixiong Xu. "TS2Vec: Towards Universal Representation of Time Series". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 8980-8987.

Zhihan Yue||Yujing Wang||Juanyong Duan||Tianmeng Yang||Congrui Huang||Yunhai Tong||Bixiong Xu. TS2Vec: Towards Universal Representation of Time Series. AAAI[Internet]. 2022[cited 2023]; 8980-8987.


ISSN: 2374-3468


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