利用专家特征进行时间序列表示的对比学习
Utilizing Expert Features for Contrastive Learning of Time-Series Representations
AI总结：
本文提出 ExpCLR，利用领域专家特征替代传统数据变换进行时间序列对比学习，以同时满足表示的两个关键性质，并在三个真实数据集上超越现有无监督和半监督方法。
摘要：
我们提出了一种将专家知识纳入时间序列表示学习的方法。我们的方法采用专家特征来替代以往对比学习方法中常用的数据变换。我们这样做是因为时间序列数据通常来自工业或医学领域，在这些领域中领域专家往往能够提供专家特征，而针对时间序列数据的数据变换通常难以确定。我们首先提出了有用时间序列表示应当满足的两个性质，并表明当前的表示学习方法并不能确保这些性质。因此，我们设计了 ExpCLR，这是一种新颖的对比学习方法，其目标利用专家特征来促使所学表示同时满足这两个性质。最后，我们在三个真实世界的时间序列数据集上证明，ExpCLR 在无监督和半监督表示学习方面均超越了多种最先进的方法。
英文摘要：
We present an approach that incorporates expert knowledge for time-series representation learning. Our method employs expert features to replace the commonly used data transformations in previous contrastive learning approaches. We do this since time-series data frequently stems from the industrial or medical field where expert features are often available from domain experts, while transformations are generally elusive for time-series data. We start by proposing two properties that useful time-series representations should fulfill and show that current representation learning approaches do not ensure these properties. We therefore devise ExpCLR, a novel contrastive learning approach built on an objective that utilizes expert features to encourage both properties for the learned representation. Finally, we demonstrate on three real-world time-series datasets that ExpCLR surpasses several state-of-the-art methods for both unsupervised and semi-supervised representation learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
