SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series
This addresses label scarcity in multivariate time series classification, which is an incremental improvement over existing semi-supervised methods.
The paper tackles the problem of label shortage in multivariate time series classification by proposing SMATE, a semi-supervised model that learns interpretable spatio-temporal representations from weakly labeled data. The method was validated on 30 public datasets, showing reliability and efficiency compared to 13 supervised and 4 semi-supervised baselines.
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.