Automated Contrastive Learning Strategy Search for Time Series

• URL: http://arxiv.org/abs/2403.12641v1
• Date: Tue, 19 Mar 2024 11:24:14 GMT
• Title: Automated Contrastive Learning Strategy Search for Time Series
• Authors: Baoyu Jing, Yansen Wang, Guoxin Sui, Jing Hong, Jingrui He, Yuqing Yang, Dongsheng Li, Kan Ren,
• Abstract summary: Contrastive Learning (CL) has become a predominant representation learning paradigm for time series. We present an Automated Machine Learning (AutoML) practice at Microsoft, which automatically learns to contrast learn representations for various time series datasets.
• Score: 48.68664732145665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, Contrastive Learning (CL) has become a predominant representation learning paradigm for time series. Most existing methods in the literature focus on manually building specific Contrastive Learning Strategies (CLS) by human heuristics for certain datasets and tasks. However, manually developing CLS usually require excessive prior knowledge about the datasets and tasks, e.g., professional cognition of the medical time series in healthcare, as well as huge human labor and massive experiments to determine the detailed learning configurations. In this paper, we present an Automated Machine Learning (AutoML) practice at Microsoft, which automatically learns to contrastively learn representations for various time series datasets and tasks, namely Automated Contrastive Learning (AutoCL). We first construct a principled universal search space of size over 3x1012, covering data augmentation, embedding transformation, contrastive pair construction and contrastive losses. Further, we introduce an efficient reinforcement learning algorithm, which optimizes CLS from the performance on the validation tasks, to obtain more effective CLS within the space. Experimental results on various real-world tasks and datasets demonstrate that AutoCL could automatically find the suitable CLS for a given dataset and task. From the candidate CLS found by AutoCL on several public datasets/tasks, we compose a transferable Generally Good Strategy (GGS), which has a strong performance for other datasets. We also provide empirical analysis as a guidance for future design of CLS.

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