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The UCR Time Series Archive

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arxiv 1810.07758 v2 pith:D3VTJHWS submitted 2018-10-17 cs.LG stat.ML

Hoang Anh Dau , Anthony Bagnall , Kaveh Kamgar , Chin-Chia Michael Yeh , Yan Zhu , Shaghayegh Gharghabi , Chotirat Ann Ratanamahatana , Eamonn Keogh This is my paper · ORCID
classification cs.LGstat.ML
keywords archivedatatimeimprovementseriessetsexpansionleast
verification ladder T0 review T1 audit T2 compute T3 formal
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The UCR Time Series Archive - introduced in 2002, has become an important resource in the time series data mining community, with at least one thousand published papers making use of at least one data set from the archive. The original incarnation of the archive had sixteen data sets but since that time, it has gone through periodic expansions. The last expansion took place in the summer of 2015 when the archive grew from 45 to 85 data sets. This paper introduces and will focus on the new data expansion from 85 to 128 data sets. Beyond expanding this valuable resource, this paper offers pragmatic advice to anyone who may wish to evaluate a new algorithm on the archive. Finally, this paper makes a novel and yet actionable claim: of the hundreds of papers that show an improvement over the standard baseline (1-nearest neighbor classification), a large fraction may be mis-attributing the reasons for their improvement. Moreover, they may have been able to achieve the same improvement with a much simpler modification, requiring just a single line of code.

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Forward citations

Cited by 5 Pith papers

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  2. Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors

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    Under a fixed leakage-free protocol, NC→LP transfer reliably helps on homophilic graphs while LP→NC helps mainly when LP is easy and NC is unsaturated; homophily and CoTask Score guide mechanism choice.

  3. CLIR-Bench: Benchmarking Multimodal Question Answering over Irregular Clinical Time Series

    cs.CL 2026-07 conditional novelty 6.0 of 10

    CLIR-Bench shows generalist and time-series LLMs struggle to ground clinical answers in sparse irregular ICU evidence, with top accuracy near 50% and weak causal evidence use.

  4. Bridging Neural Networks and Dynamic Time Warping for Adaptive Time Series Classification

    cs.LG 2025-07 conditional novelty 5.0 of 10

    A recurrent network built from the DTW recurrence, using compressed prototypes, often beats DTW-kNN in low-resource settings and stays close to deep learning baselines on UCR benchmarks.

  5. On the Feasibility of Vision-Language Models for Time-Series Classification

    cs.AI 2024-12 reject novelty 5.0 of 10

    Fine-tuning a vision-language model for one or two epochs on line plots plus text can classify several UCR time-series datasets, but the 'competitive' claim is never tested against standard baselines.

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