mlsanityData to 14 Oct 2026

The UCR Time Series Archive

arXiv 1810.07758 ↗First posted 17 Oct 2018cs.LG stat.MLcited by 431

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Abstract (arXiv)

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.

Key facts. Cited by 431 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Oct 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 30; 2022: 49; 2023: 88; 2024: 86; 2025: 86; 2026: 92.

431 papers.

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  1. 2021TS2Vec: Towards Universal Representation of Time Seriescited by 471
  2. 2021Monash Time Series Forecasting Archivecited by 267
  3. 2024Timer: Generative Pre-trained Transformers Are Large Time Series Modelscited by 208
  4. 2023TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Seriescited by 169
  5. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  6. 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspectivecited by 127
  7. 2021HIVE-COTE 2.0: a new meta ensemble for time series classificationcited by 118
  8. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 110
  9. 2023DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detectioncited by 103
  10. 2023Bake off redux: a review and experimental evaluation of recent time series classification algorithmscited by 101
  11. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 99
  12. 2021Voice2Series: Reprogramming Acoustic Models for Time Series Classificationcited by 91
38 more
  1. 2021MultiRocket: Multiple pooling operators and transformations for fast and effective time series classificationcited by 90
  2. 2023Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglementcited by 90
  3. 2021Chaos as an interpretable benchmark for forecasting and data-driven modellingcited by 88
  4. 2021A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challengescited by 69
  5. 2023Deep Learning for Time Series Classification and Extrinsic Regression: A Current Surveycited by 68
  6. 2022Model Reprogramming: Resource-Efficient Cross-Domain Machine Learningcited by 64
  7. 2023Ti-MAE: Self-Supervised Masked Time Series Autoencoderscited by 63
  8. 2025TAB: Unified Benchmarking of Time Series Anomaly Detection Methodscited by 60
  9. 2025OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLMcited by 59
  10. 2021Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advancescited by 54
  11. 2022Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Seriescited by 54
  12. 2022HYDRA: Competing convolutional kernels for fast and accurate time series classificationcited by 54
  13. 2022Evaluation of post-hoc interpretability methods in time-series classificationcited by 53
  14. 2023Time Series Contrastive Learning with Information-Aware Augmentationscited by 52
  15. 2023A Survey on Time-Series Pre-Trained Modelscited by 52
  16. 2023TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learningcited by 52
  17. 2023Soft Contrastive Learning for Time Seriescited by 51
  18. 2023LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Versioncited by 50
  19. 2024Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Datacited by 36
  20. 2024A Survey of Deep Learning and Foundation Models for Time Series Forecastingcited by 33
  21. 2024TimeX++: Learning Time-Series Explanations with Information Bottleneckcited by 33
  22. 2024Towards Neural Scaling Laws for Time Series Foundation Modelscited by 33
  23. 2024Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfercited by 32
  24. 2024Universal Time-Series Representation Learning: A Surveycited by 30
  25. 2023An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Setcited by 29
  26. 2025LightGTS: A Lightweight General Time Series Forecasting Modelcited by 29
  27. 2025Aurora: Towards Universal Generative Multimodal Time Series Forecastingcited by 29
  28. 2023Toward a Foundation Model for Time Series Datacited by 28
  29. 2021The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classificationcited by 26
  30. 2023TSGBench: Time Series Generation Benchmarkcited by 26
  31. 2022Domain Adaptation for Time-Series Classification to Mitigate Covariate Shiftcited by 24
  32. 2022Explainable AI for clinical and remote health applications: a survey on tabular and time series datacited by 24
  33. 2025Mantis: Lightweight Foundation Model for Time Series Classificationcited by 24
  34. 2021Learning Discriminative Prototypes with Dynamic Time Warpingcited by 23
  35. 2023Inherently Interpretable Time Series Classification via Multiple Instance Learningcited by 23
  36. 2024Empowering Time Series Analysis with Foundation Models: A Comprehensive Surveycited by 23
  37. 2021Random Dilated Shapelet Transform: A New Approach for Time Series Shapeletscited by 22
  38. 2022Dynamic Sparse Network for Time Series Classification: Learning What to "see''cited by 22

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