mlsanityData to 7 Oct 2026

Bake off redux: a review and experimental evaluation of recent time series classification algorithms

arXiv 2304.13029 ↗First posted 25 Apr 2023cs.LGcited by 99

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Abstract (arXiv)

In 2017, a research paper compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the University of California, Riverside (UCR) archive. This study, commonly referred to as a `bake off', identified that only nine algorithms performed significantly better than the Dynamic Time Warping (DTW) and Rotation Forest benchmarks that were used. The study categorised each algorithm by the type of feature they extract from time series data, forming a taxonomy of five main algorithm types. This categorisation of algorithms alongside the provision of code and accessible results for reproducibility has helped fuel an increase in popularity of the TSC field. Over six years have passed since this bake off, the UCR archive has expanded to 112 datasets and there have been a large number of new algorithms proposed. We revisit the bake off, seeing how each of the proposed categories have advanced since the original publication, and evaluate the performance of newer algorithms against the previous best-of-category using an expanded UCR archive. We extend the taxonomy to include three new categories to reflect recent developments. Alongside the originally proposed distance, interval, shapelet, dictionary and hybrid based algorithms, we compare newer convolution and feature based algorithms as well as deep learning approaches. We introduce 30 classification datasets either recently donated to the archive or reformatted to the TSC format, and use these to further evaluate the best performing algorithm from each category. Overall, we find that two recently proposed algorithms, Hydra+MultiROCKET and HIVE-COTEv2, perform significantly better than other approaches on both the current and new TSC problems.

Key facts. Cited by 99 papers in the map (data through 7 Oct 2026). Builds on 31 of its 91 references that are in the map. First posted on arXiv 25 Apr 2023.

Builds on

31 of 91 references.

References · 31

  1. 2018Deep learning for time series classification: a reviewcited by 575
  2. 2018The UCR Time Series Archivecited by 421
  3. 2019InceptionTime: Finding AlexNet for Time Series Classificationcited by 386
  4. 2016Time Series Classification from Scratch with Deep Neural Networks: A Strong Baselinecited by 358
  5. 2019ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernelscited by 336
  6. 2020MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classificationcited by 186
  7. 2020Benchmarking Multivariate Time Series Classification Algorithmscited by 145
  8. 2021HIVE-COTE 2.0: a new meta ensemble for time series classificationcited by 118
  9. 2015Should we really use post-hoc tests based on mean-ranks?cited by 116
  10. 2019catch22: CAnonical Time-series CHaracteristicscited by 103
  11. 2021MultiRocket: Multiple pooling operators and transformations for fast and effective time series classificationcited by 89
  12. 2013A Time Series Forest for Classification and Feature Extractioncited by 69
19 more
  1. 2023Deep Learning for Time Series Classification and Extrinsic Regression: A Current Surveycited by 68
  2. 2019TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classificationcited by 67
  3. 2022HYDRA: Competing convolutional kernels for fast and accurate time series classificationcited by 53
  4. 2020The Canonical Interval Forest (CIF) Classifier for Time Series Classificationcited by 48
  5. 2017Fast and Accurate Time Series Classification with WEASELcited by 47
  6. 2018Proximity Forest: An effective and scalable distance-based classifier for time seriescited by 46
  7. 2018A review on distance based time series classificationcited by 40
  8. 2016shapeDTW: shape Dynamic Time Warpingcited by 35
  9. 2020Interpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic Representationscited by 31
  10. 2021The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classificationcited by 26
  11. 2021Random Dilated Shapelet Transform: A New Approach for Time Series Shapeletscited by 22
  12. 2019Scalable Dictionary Classifiers for Time Series Classificationcited by 20
  13. 2022The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifiercited by 18
  14. 2018From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifierscited by 14
  15. 2022A Review and Evaluation of Elastic Distance Functions for Time Series Clusteringcited by 14
  16. 2020A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0cited by 12
  17. 2018Is rotation forest the best classifier for problems with continuous features?cited by 11
  18. 2021Fast, Accurate and Interpretable Time Series Classification Through Randomizationcited by 5
  19. 2020Detecting Electric Devices in 3D Images of Bagscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 12; 2024: 28; 2025: 25; 2026: 34.

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  1. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  2. 2024UniTS: A Unified Multi-Task Time Series Modelcited by 93
  3. 2024aeon: a Python toolkit for learning from time seriescited by 46
  4. 2023An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Setcited by 27
  5. 2024ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysiscited by 26
  6. 2023Inherently Interpretable Time Series Classification via Multiple Instance Learningcited by 23
  7. 2023QUANT: A Minimalist Interval Method for Time Series Classificationcited by 15
  8. 2025Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approachcited by 14
  9. 2024Cross-Domain Pre-training with Language Models for Transferable Time Series Representationscited by 12
  10. 2024Advancing Time Series Classification with Multimodal Language Modelingcited by 10
  11. 2025Learning Soft Sparse Shapes for Efficient Time-Series Classificationcited by 10
  12. 2023Unsupervised Feature Based Algorithms for Time Series Extrinsic Regressioncited by 9
38 more
  1. 2024Look Into the LITE in Deep Learning for Time Series Classificationcited by 9
  2. 2025MAESTRO : Adaptive Sparse Attention and Robust Learning for Multimodal Dynamic Time Seriescited by 9
  3. 2025Pre-trained Forecasting Models: Strong Zero-Shot Feature Extractors for Time Series Classificationcited by 9
  4. 2023Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fieldscited by 8
  5. 2024Exploring Kolmogorov-Arnold Networks for Interpretable Time Series Classificationcited by 8
  6. 2024On the Feasibility of Vision-Language Models for Time-Series Classificationcited by 8
  7. 2023Robust Explainer Recommendation for Time Series Classificationcited by 7
  8. 2023Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernelscited by 7
  9. 2024QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models -- Extended Versioncited by 7
  10. 2025MONSTER: Monash Scalable Time Series Evaluation Repositorycited by 6
  11. 2024DeNOTS: Stable Deep Neural ODEs for Time Seriescited by 5
  12. 2025MASCOTS: Model-Agnostic Symbolic COunterfactual explanations for Time Seriescited by 5
  13. 2023Finding Foundation Models for Time Series Classification with a PreText Taskcited by 4
  14. 2024Phase-driven Domain Generalizable Learning for Nonstationary Time Seriescited by 4
  15. 2024LLM-ABBA: Understanding time series via symbolic approximationcited by 4
  16. 2026RocketPFN: Accurate Time Series Classification via In-Context Learningcited by 4
  17. 2024Early Classification of Time Series: A Survey and Benchmarkcited by 3
  18. 2024Dataset-Driven Channel Masks in Transformers for Multivariate Time Seriescited by 3
  19. 2025CLaP -- State Detection from Time Seriescited by 3
  20. 2026A Unified Shape-Aware Foundation Model for Time Series Classificationcited by 3
  21. 2026The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classificationcited by 3
  22. 2024Nonlinear time-series embedding by monotone variational inequalitycited by 2
  23. 2024Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classificationcited by 2
  24. 2024Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble: An Improved ROCKET Algorithm for Multivariate Time Series Analysiscited by 2
  25. 2024AI-driven Java Performance Testing: Balancing Result Quality with Testing Timecited by 2
  26. 2024Time series classification with random convolution kernels: pooling operators and input representations mattercited by 2
  27. 2024On time series clustering with k-meanscited by 2
  28. 2024Using matrix-product states for time-series machine learningcited by 2
  29. 2025Representation Learning of Limit Order Book: A Comprehensive Study and Benchmarkingcited by 2
  30. 2025TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learningcited by 2
  31. 2025Why Do Class-Dependent Evaluation Effects Occur with Time Series Feature Attributions? A Synthetic Data Investigationcited by 2
  32. 2025Counterfactual Explanations for Time Series Should be Human-Centered and Temporally Coherent in Interventionscited by 2
  33. 2026InstructTime++: Time Series Classification with Multimodal Language Modeling via Implicit Feature Enhancementcited by 2
  34. 2023Convolutional and Deep Learning based techniques for Time Series Ordinal Classificationcited by 1
  35. 2023FRUITS: Feature Extraction Using Iterated Sums for Time Series Classificationcited by 1
  36. 2024Time-Series Forecasting in Smart Manufacturing Systems: An Experimental Evaluation of the State-of-the-art Algorithmscited by 1
  37. 2024Rock the KASBA: Blazingly Fast and Accurate Time Series Clusteringcited by 1
  38. 2025A 1-D CNN inference engine for constrained platformscited by 1

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