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.

翻译：近年来，对比学习已成为时间序列领域主流的表示学习范式。现有文献中的大多数方法依赖于人工启发式方法，针对特定数据集和任务手动构建具体的对比学习策略。然而，手动开发对比学习策略通常需要对数据集和任务具备充分的先验知识（例如医疗场景下对医学时间序列的专业认知），同时需要投入大量人力与实验以确定详细的学习配置。本文介绍了微软的一项自动化机器学习实践——AutoCL，它能自动为各类时间序列数据集和任务学习对比表示。我们首先构建了一个规模超过3×10^12种组合的原理性通用搜索空间，涵盖数据增强、嵌入变换、对比对构建和对比损失函数。进一步，我们引入了一种高效的强化学习算法，该算法基于验证任务性能对对比学习策略进行优化，从而在搜索空间内获取更有效的策略。在多个真实世界任务与数据集上的实验结果表明，AutoCL能够自动为给定数据集和任务找到合适的对比学习策略。通过AutoCL在多个公共数据集/任务上发现的候选策略，我们归纳出一种可迁移的通用优质策略，该策略在其他数据集上展现出强劲性能。此外，本文还提供了实证分析，为未来对比学习策略的设计提供指导。