mlsanityData to 7 Oct 2026

The Canonical Interval Forest (CIF) Classifier for Time Series Classification

arXiv 2008.09172 ↗First posted 20 Aug 2020cs.LG eess.SPcited by 48

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Abstract (arXiv)

Time series classification (TSC) is home to a number of algorithm groups that utilise different kinds of discriminatory patterns. One of these groups describes classifiers that predict using phase dependant intervals. The time series forest (TSF) classifier is one of the most well known interval methods, and has demonstrated strong performance as well as relative speed in training and predictions. However, recent advances in other approaches have left TSF behind. TSF originally summarises intervals using three simple summary statistics. The `catch22' feature set of 22 time series features was recently proposed to aid time series analysis through a concise set of diverse and informative descriptive characteristics. We propose combining TSF and catch22 to form a new classifier, the Canonical Interval Forest (CIF). We outline additional enhancements to the training procedure, and extend the classifier to include multivariate classification capabilities. We demonstrate a large and significant improvement in accuracy over both TSF and catch22, and show it to be on par with top performers from other algorithmic classes. By upgrading the interval-based component from TSF to CIF, we also demonstrate a significant improvement in the hierarchical vote collective of transformation-based ensembles (HIVE-COTE) that combines different time series representations. HIVE-COTE using CIF is significantly more accurate on the UCR archive than any other classifier we are aware of and represents a new state of the art for TSC.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Aug 2020.

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Citing papers per year: 2021: 5; 2022: 8; 2023: 18; 2024: 8; 2025: 5; 2026: 4.

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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. 2023LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Versioncited by 49
  6. 2024aeon: a Python toolkit for learning from time seriescited by 46
  7. 2023Improving Position Encoding of Transformers for Multivariate Time Series Classificationcited by 44
  8. 2023An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Setcited by 27
  9. 2021Random Dilated Shapelet Transform: A New Approach for Time Series Shapeletscited by 22
  10. 2023NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretrainingcited by 20
  11. 2022The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifiercited by 18
  12. 2023QUANT: A Minimalist Interval Method for Time Series Classificationcited by 15
36 more
  1. 2022HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computingcited by 13
  2. 2023Unsupervised Feature Based Algorithms for Time Series Extrinsic Regressioncited by 9
  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. 2022Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Descriptioncited by 7
  6. 2023Appliance Detection Using Very Low-Frequency Smart Meter Time Seriescited by 7
  7. 2023Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithmscited by 7
  8. 2021Fast, Accurate and Interpretable Time Series Classification Through Randomizationcited by 5
  9. 2023Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time seriescited by 5
  10. 2022Robust Augmentation for Multivariate Time Series Classificationcited by 4
  11. 2023POCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspectivecited by 4
  12. 2023Automatic Feature Engineering for Time Series Classification: Evaluation and Discussioncited by 3
  13. 2024Active Region-based Flare Forecasting with Sliding Window Multivariate Time Series Forest Classifierscited by 3
  14. 2026The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classificationcited by 3
  15. 2026MambaSL: Exploring Single-Layer Mamba for Time Series Classificationcited by 3
  16. 2024Towards Hybrid Embedded Feature Selection and Classification Approach with Slim-TSFcited by 2
  17. 2024VSFormer: Value and Shape-Aware Transformer with Prior-Enhanced Self-Attention for Multivariate Time Series Classificationcited by 2
  18. 2025Revisit Time Series Classification Benchmark: The Impact of Temporal Information for Classificationcited by 2
  19. 2023Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning Infusioncited by 1
  20. 2023FedST: Secure Federated Shapelet Transformation for Time Series Classificationcited by 1
  21. 2023Convolutional and Deep Learning based techniques for Time Series Ordinal Classificationcited by 1
  22. 2024Federated Learning for Tabular Data using TabNet: A Vehicular Use-Casecited by 1
  23. 2025Label-Guided Imputation via Forest-Based Proximities for Improved Time Series Classificationcited by 1
  24. 2026In-Context Time Series Classification with Random Convolutional Featurescited by 1
  25. 2022COSTI: a New Classifier for Sequences of Temporal Intervalscited by 0
  26. 2022Explainable classification of astronomical uncertain time seriescited by 0
  27. 2022An Empirical Evaluation of Multivariate Time Series Classification with Input Transformation across Different Dimensionscited by 0
  28. 2023Multivariate time series classification with dual attention networkcited by 0
  29. 2023ChemTime: Rapid and Early Classification for Multivariate Time Series Classification of Chemical Sensorscited by 0
  30. 2024Castor: Competing shapelets for fast and accurate time series classificationcited by 0
  31. 2024ECRTime: Ensemble Integration of Classification and Retrieval for Time Series Classificationcited by 0
  32. 2024Utilizing Data Fingerprints for Privacy-Preserving Algorithm Selection in Time Series Classification: Performance and Uncertainty Estimation on Unseen Datasetscited by 0
  33. 2025Towards Explainable Sequential Learningcited by 0
  34. 2025Data Classification with Dynamically Growing and Shrinking Neural Networkscited by 0
  35. 2025Guided by Stars: Interpretable Concept Learning Over Time Series via Temporal Logic Semanticscited by 0
  36. 2026TimeSliver : Symbolic-Linear Decomposition for Explainable Time Series Classificationcited by 0