%0 Generic
%A Jiao, Yiru
%A Calvert, Simeon
%A van Cranenburgh, Sander
%A van Lint, Hans
%D 2025
%T Data underlying the publication: Structure-preserving contrastive learning for spatial time series
%U 
%R 10.4121/3b8cf098-c2ce-49b1-8e36-74b37872aaa6.v1
%K Contrastive learning
%K representation learning
%K time series
%K spatio-temporal data
%K traffic interaction
%X <p>This dataset includes the resulting data of the research: Structure-preserving contrastive learning for spatial time series. It includes precomputed distance matrices, logs and results from hyperparameter grid search, trained encoder checkpoints, as well as evaluation metrics for UEA classification and traffic prediction tasks. The research is experimental and focuses on enhancing self-supervised contrastive learning by preserving fine‐grained spatio-temporal similarity structures. The proposed methods are applied to public UEA archive datasets of multivariate time series and specialised macro- and micro-traffic datasets. The scripts that produced these data are open-sourced at https://github.com/Yiru-Jiao/SPCLT</p>
%I 4TU.ResearchData