mlsanityData to 7 Oct 2026

TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification

arXiv 1906.10329 ↗First posted 25 Jun 2019cs.LG stat.MLcited by 67

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Abstract (arXiv)

Time Series Classification (TSC) has seen enormous progress over the last two decades. HIVE-COTE (Hierarchical Vote Collective of Transformation-based Ensembles) is the current state of the art in terms of classification accuracy. HIVE-COTE recognizes that time series data are a specific data type for which the traditional attribute-value representation, used predominantly in machine learning, fails to provide a relevant representation. HIVE-COTE combines multiple types of classifiers: each extracting information about a specific aspect of a time series, be it in the time domain, frequency domain or summarization of intervals within the series. However, HIVE-COTE (and its predecessor, FLAT-COTE) is often infeasible to run on even modest amounts of data. For instance, training HIVE-COTE on a dataset with only 1,500 time series can require 8 days of CPU time. It has polynomial runtime with respect to the training set size, so this problem compounds as data quantity increases. We propose a novel TSC algorithm, TS-CHIEF (Time Series Combination of Heterogeneous and Integrated Embedding Forest), which rivals HIVE-COTE in accuracy but requires only a fraction of the runtime. TS-CHIEF constructs an ensemble classifier that integrates the most effective embeddings of time series that research has developed in the last decade. It uses tree-structured classifiers to do so efficiently. We assess TS-CHIEF on 85 datasets of the University of California Riverside (UCR) archive, where it achieves state-of-the-art accuracy with scalability and efficiency. We demonstrate that TS-CHIEF can be trained on 130k time series in 2 days, a data quantity that is beyond the reach of any TSC algorithm with comparable accuracy.

Key facts. Cited by 67 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jun 2019.

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Citing papers per year: 2021: 13; 2022: 11; 2023: 21; 2024: 12; 2025: 4; 2026: 6.

67 papers.

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  1. 2021HIVE-COTE 2.0: a new meta ensemble for time series classificationcited by 118
  2. 2023Bake off redux: a review and experimental evaluation of recent time series classification algorithmscited by 99
  3. 2021MultiRocket: Multiple pooling operators and transformations for fast and effective time series classificationcited by 89
  4. 2022HYDRA: Competing convolutional kernels for fast and accurate time series classificationcited by 53
  5. 2023A Survey on Time-Series Pre-Trained Modelscited by 52
  6. 2023An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Setcited by 27
  7. 2021The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classificationcited by 26
  8. 2021Random Dilated Shapelet Transform: A New Approach for Time Series Shapeletscited by 22
  9. 2022The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifiercited by 18
  10. 2023WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classificationcited by 15
  11. 2022HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computingcited by 13
  12. 2024Cross-Domain Pre-training with Language Models for Transferable Time Series Representationscited by 12
38 more
  1. 2022dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classificationcited by 11
  2. 2024Advancing Time Series Classification with Multimodal Language Modelingcited by 10
  3. 2021Elastic Similarity and Distance Measures for Multivariate Time Seriescited by 8
  4. 2023Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fieldscited by 8
  5. 2021MrSQM: Fast Time Series Classification with Symbolic Representationscited by 7
  6. 2022Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Descriptioncited by 7
  7. 2023Appliance Detection Using Very Low-Frequency Smart Meter Time Seriescited by 7
  8. 2023Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernelscited by 7
  9. 2023Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithmscited by 7
  10. 2023Unleashing the Power of Shared Label Structures for Human Activity Recognitioncited by 6
  11. 2021Fast, Accurate and Interpretable Time Series Classification Through Randomizationcited by 5
  12. 2022Rethinking Attention Mechanism in Time Series Classificationcited by 5
  13. 2023Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time seriescited by 5
  14. 2026RocketPFN: Accurate Time Series Classification via In-Context Learningcited by 4
  15. 2021Plotting time: On the usage of CNNs for time series classificationcited by 3
  16. 2021Early Abandoning and Pruning for Elastic Distances including Dynamic Time Warpingcited by 3
  17. 2023Parameterizing the cost function of Dynamic Time Warping with application to time series classificationcited by 3
  18. 2023Automatic Feature Engineering for Time Series Classification: Evaluation and Discussioncited by 3
  19. 2024Data Augmentation for Multivariate Time Series Classification: An Experimental Studycited by 3
  20. 2021An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series Classificationcited by 2
  21. 2021Amercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time Warpingcited by 2
  22. 2022A Large-Scale Annotated Multivariate Time Series Aviation Maintenance Dataset from the NGAFIDcited by 2
  23. 2023A hybrid feature learning approach based on convolutional kernels for ATM fault prediction using event-log datacited by 2
  24. 2024Capturing Temporal Components for Time Series Classificationcited by 2
  25. 2024Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble: An Improved ROCKET Algorithm for Multivariate Time Series Analysiscited by 2
  26. 2026InstructTime++: Time Series Classification with Multimodal Language Modeling via Implicit Feature Enhancementcited by 2
  27. 2021Uncertain Time Series Classification With Shapelet Transformcited by 1
  28. 2022Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Seriescited by 1
  29. 2023Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning Infusioncited by 1
  30. 2023RITA: Group Attention is All You Need for Timeseries Analyticscited by 1
  31. 2023Convolutional and Deep Learning based techniques for Time Series Ordinal Classificationcited by 1
  32. 2023FRUITS: Feature Extraction Using Iterated Sums for Time Series Classificationcited by 1
  33. 2023Multi-Granularity Framework for Unsupervised Representation Learning of Time Seriescited by 1
  34. 2024Rank Supervised Contrastive Learning for Time Series Classificationcited by 1
  35. 2024Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approachcited by 1
  36. 2024Time Series Representation Learning with Supervised Contrastive Temporal Transformercited by 1
  37. 2024A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretrainingcited by 1
  38. 2025RockNet: Distributed Learning on Ultra-Low-Power Devicescited by 1

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