Data underlying the publication: Structure-preserving contrastive learning for spatial time series

DOI:10.4121/3b8cf098-c2ce-49b1-8e36-74b37872aaa6.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/3b8cf098-c2ce-49b1-8e36-74b37872aaa6

Datacite citation style

Jiao, Yiru; Simeon Calvert; van Cranenburgh, Sander; van Lint, Hans (2025): Data underlying the publication: Structure-preserving contrastive learning for spatial time series. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/3b8cf098-c2ce-49b1-8e36-74b37872aaa6.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

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

History

  • 2025-06-11 first online, published, posted

Publisher

4TU.ResearchData

Format

HDF5, CSV, NPY, PTH

Funding

  • TU Delft AI Labs programme [more info...] Delft University of Technology

Organizations

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Transport and Planning, Traffic Systems Engineering

DATA

Files (2)