Self-Supervised Learning for Time Series: Contrastive or Generative?

arXiv:2403.09809v1 Announce Type: new
Abstract: Self-supervised learning (SSL) has recently emerged as a powerful approach to learning representations from large-scale unlabeled data, showing promising results in time series analysis. The self-supervised representation learning can be categorized into two mainstream: contrastive and generative. In this paper, we will present a comprehensive comparative study between contrastive and generative methods in time series. We first introduce the basic frameworks for contrastive and generative SSL, respectively, and discuss how to obtain the supervision signal …

arxiv cs.ai cs.et cs.lg generative self-supervised learning series supervised learning time series type