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.

翻译：我们提出一种方法,将专家知识纳入时间序列代表制学习。我们的方法使用专家特征来取代以往对比式学习方法中常用的数据转换。我们这样做是因为时间序列数据经常来自工业或医疗领域,该领域的专家特征往往来自领域专家,而时间序列数据则一般难以实现转变。我们首先提出两个有用的时间序列代表制应该实现的属性,并表明目前的代表性学习方法并不能确保这些属性。因此,我们设计了ExcLR,这是一个新颖的对比式学习方法,它建立在利用专家特征鼓励两种属性进行学习上。最后,我们用三种真实世界时间序列数据集来证明,ExcLR超越了非监督性和半监督性代表制学习的几种最先进的方法。