Label-efficient Time Series Representation Learning: A Review

• URL: http://arxiv.org/abs/2302.06433v4
• Date: Wed, 24 Jul 2024 03:43:32 GMT
• Title: Label-efficient Time Series Representation Learning: A Review
• Authors: Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Xiaoli Li,
• Abstract summary: Label-efficient time series representation learning 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. 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.
• Score: 19.218833228063392
• License: http://creativecommons.org/licenses/by/4.0/
• 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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