Learning from Multivariate Time Series (MTS) has attracted widespread attention in recent years. In particular, label shortage is a real challenge for the classification task on MTS, considering its complex dimensional and sequential data structure. Unlike self-training and positive unlabeled learning that rely on distance-based classifiers, in this paper, we propose SMATE, a novel semi-supervised model for learning the interpretable Spatio-Temporal representation from weakly labeled MTS. We validate empirically the learned representation on 30 public datasets from the UEA MTS archive. We compare it with 13 state-of-the-art baseline methods for fully supervised tasks and four baselines for semi-supervised tasks. The results show the reliability and efficiency of our proposed method.
@article{arxiv.2110.00578,
title = {SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series},
author = {Jingwei Zuo and Karine Zeitouni and Yehia Taher},
journal= {arXiv preprint arXiv:2110.00578},
year = {2021}
}