TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis

Summary: CSL: the first unsupervised contrastive learner of general, interpretable shapelet-based time-series representations, improving classification, clustering, and anomaly detection. TimeCSL: an end-to-end interactive system to explore learned shapelets for unified analysis. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13690
Venue: VLDB
Year: 2024
Pagerank: 4.3254416e-05
Overall Rank: 9,559 | 33.50%
DOI: 10.14778/3685800.3685907

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,035	SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine	2026	SIGMOD	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
13,156	A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning	2024	VLDB	-

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