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arXiv · 2302.06433

Label-efficient Time Series Representation Learning: A Review

Emadeldeen Eldele·Mohamed Ragab·Zhenghua Chen·Min Wu·Chee-Keong Kwoh·Xiaoli Li
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Abstract

Label-efficient time series representation learning, which aims to learn effective representations with limited labeled data, is crucial for deploying deep learning models in real-world applications. To address the scarcity of labeled time series data, various strategies, e.g., transfer learning, self-supervised learning, and semi-supervised learning, have been developed. In this survey, we introduce a novel taxonomy for the first time, categorizing existing approaches as in-domain or cross-domain, based on their reliance on external data sources or not. Furthermore, we present a review of the recent advances in each strategy, conclude the limitations of current methodologies, and suggest future research directions that promise further improvements in the field.

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Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Xiaoli Li. 2023-02-13. Label-efficient Time Series Representation Learning: A Review. https://doi.org/10.1109/tai.2024.3430236

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