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arXiv · 2110.00578

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

Jingwei Zuo·Karine Zeitouni·Yehia Taher
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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 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.

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Jingwei Zuo, Karine Zeitouni, Yehia Taher. 2021-10-01. SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series. https://arxiv.org/abs/2110.00578

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