Time Series Representation Learning with Supervised Contrastive Temporal Transformer

arXiv:2403.10787v1 Announce Type: new
Abstract: Finding effective representations for time series data is a useful but challenging task. Several works utilize self-supervised or unsupervised learning methods to address this. However, there still remains the open question of how to leverage available label information for better representations. To answer this question, we exploit pre-existing techniques in time series and representation learning domains and develop a simple, yet novel fusion model, called: \textbf{S}upervised \textbf{CO}ntrastive \textbf{T}emporal \textbf{T}ransformer (SCOTT). We first investigate suitable augmentation …

abstract arxiv cs.ai cs.lg data however information question representation representation learning series temporal time series transformer type unsupervised unsupervised learning