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

arXiv: https://arxiv.org/abs/2110.00578 · HTML: https://mlsanity.com/paper/2110.00578/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [The UEA multivariate time series classification archive, 2018](https://mlsanity.com/paper/1811.00075/) (2018)
- [Unsupervised Scalable Representation Learning for Multivariate Time Series](https://mlsanity.com/paper/1901.10738/) (2019)
- [Benchmarking Multivariate Time Series Classification Algorithms](https://mlsanity.com/paper/2007.13156/) (2020)
- [Multivariate LSTM-FCNs for Time Series Classification](https://mlsanity.com/paper/1801.04503/) (2018)
- [Multivariate Time Series Classification with WEASEL+MUSE](https://mlsanity.com/paper/1711.11343/) (2017)

## Built on by (most cited first, 9 of 9)

- [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://mlsanity.com/paper/2302.02515/) (2023)
- [Label-efficient Time Series Representation Learning: A Review](https://mlsanity.com/paper/2302.06433/) (2023)
- [Graph Convolutional Networks for Traffic Forecasting with Missing Values](https://mlsanity.com/paper/2212.06419/) (2022)
- [On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge](https://mlsanity.com/paper/2302.09310/) (2023)
- [Unleashing Realistic Air Quality Forecasting: Introducing the Ready-to-Use PurpleAirSF Dataset](https://mlsanity.com/paper/2306.13948/) (2023)
- [MAGNETO: Edge AI for Human Activity Recognition -- Privacy and Personalization](https://mlsanity.com/paper/2402.07180/) (2024)
- [Re-thinking Human Activity Recognition with Hierarchy-aware Label Relationship Modeling](https://mlsanity.com/paper/2403.05557/) (2024)
- [Alzheimer's disease detection in PSG signals](https://mlsanity.com/paper/2404.03549/) (2024)
- [Causal and Local Correlations Based Network for Multivariate Time Series Classification](https://mlsanity.com/paper/2411.18008/) (2024)

## Abstract (arXiv)

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.