Structure-preserving contrastive learning for spatial time series

Yiru Jiao, Sander van Cranenburgh, Simeon C. Calvert

Keywords: time series, contrastive learning, spatio-temporal data, traffic interaction

TL;DR: Preserving certain structures of similarity relations in spatio-temporal data can improve downstream task performance via contrastive learning.

Abstract: Informative representations enhance model performance and generalisability in downstream tasks. However, learning self-supervised representations for spatially characterised time series, like traffic interactions, poses challenges as it requires maintaining fine-grained similarity relations in the latent space. In this study, we incorporate two structure-preserving regularisers for the contrastive learning of spatial time series: one regulariser preserves the topology of similarities between instances, and the other preserves the graph geometry of similarities across spatial and temporal dimensions. To balance contrastive learning and structure preservation, we propose a dynamic mechanism that adaptively weighs the trade-off and stabilises training. We conduct experiments on multivariate time series classification, as well as macroscopic and microscopic traffic prediction. For all three tasks, our approach preserves the structures of similarity relations more effectively and improves state-of-the-art task performances. This approach can be applied to an arbitrary encoder and is particularly beneficial for time series with spatial or geographical features. Our code is attached as supplementary material, which will be made openly available with all resulting data after review.

Primary Area: learning on time series and dynamical systems

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Submission Number: 8006