SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series

• URL: http://arxiv.org/abs/2110.00578v1
• Date: Fri, 1 Oct 2021 17:59:46 GMT
• Title: SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series
• Authors: Jingwei Zuo, Karine Zeitouni and Yehia Taher
• Abstract summary: We propose SMATE, a novel semi-supervised model for learning the interpretable Spatio-Temporal representation from weakly labeled MTS. We validate the learned representation on 22 public datasets from the UEA MTS archive.
• Score: 0.6445605125467572
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 22 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.

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