mlsanityData to 14 Oct 2026

The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification

arXiv 2105.03841 ↗First posted 9 May 2021cs.LGcited by 26

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Abstract (arXiv)

Using bag of words representations of time series is a popular approach to time series classification. These algorithms involve approximating and discretising windows over a series to form words, then forming a count of words over a given dictionary. Classifiers are constructed on the resulting histograms of word counts. A 2017 evaluation of a range of time series classifiers found the bag of symbolic-fourier approximation symbols (BOSS) ensemble the best of the dictionary based classifiers. It forms one of the components of hierarchical vote collective of transformation-based ensembles (HIVE-COTE), which represents the current state of the art. Since then, several new dictionary based algorithms have been proposed that are more accurate or more scalable (or both) than BOSS. We propose a further extension of these dictionary based classifiers that combines the best elements of the others combined with a novel approach to constructing ensemble members based on an adaptive Gaussian process model of the parameter space. We demonstrate that the temporal dictionary ensemble (TDE) is more accurate than other dictionary based approaches. Furthermore, unlike the other classifiers, if we replace BOSS in HIVE-COTE with TDE, HIVE-COTE is significantly more accurate. We also show this new version of HIVE-COTE is significantly more accurate than the current best deep learning approach, a recently proposed hybrid tree ensemble and a recently introduced competitive classifier making use of highly randomised convolutional kernels. This advance represents a new state of the art for time series classification.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). Builds on 10 of its 19 references that are in the map. First posted on arXiv 9 May 2021.

Builds on

10 of 19 references.

References · 10

  1. 2018The UCR Time Series Archivecited by 431
  2. 2019InceptionTime: Finding AlexNet for Time Series Classificationcited by 387
  3. 2019ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernelscited by 339
  4. 2015Should we really use post-hoc tests based on mean-ranks?cited by 116
  5. 2013A Time Series Forest for Classification and Feature Extractioncited by 69
  6. 2019TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classificationcited by 67
  7. 2017Fast and Accurate Time Series Classification with WEASELcited by 47
  8. 2019Scalable Dictionary Classifiers for Time Series Classificationcited by 20
  9. 2018From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifierscited by 14
  10. 2020A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0cited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 14; 2024: 3; 2025: 0; 2026: 3.

26 papers.

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  1. 2023Bake off redux: a review and experimental evaluation of recent time series classification algorithmscited by 101
  2. 2022HYDRA: Competing convolutional kernels for fast and accurate time series classificationcited by 54
  3. 2024aeon: a Python toolkit for learning from time seriescited by 47
  4. 2023An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Setcited by 29
  5. 2021Random Dilated Shapelet Transform: A New Approach for Time Series Shapeletscited by 22
  6. 2023NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretrainingcited by 20
  7. 2022The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifiercited by 18
  8. 2023WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classificationcited by 15
  9. 2023QUANT: A Minimalist Interval Method for Time Series Classificationcited by 15
  10. 2023Unsupervised Feature Based Algorithms for Time Series Extrinsic Regressioncited by 9
  11. 2022Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Descriptioncited by 8
  12. 2021Fast, Accurate and Interpretable Time Series Classification Through Randomizationcited by 5
14 more
  1. 2023Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time seriescited by 5
  2. 2023POCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspectivecited by 5
  3. 2026The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classificationcited by 4
  4. 2023Automatic Feature Engineering for Time Series Classification: Evaluation and Discussioncited by 3
  5. 2023Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning Infusioncited by 1
  6. 2023FedST: Secure Federated Shapelet Transformation for Time Series Classificationcited by 1
  7. 2023A Dictionary-based approach to Time Series Ordinal Classificationcited by 1
  8. 2023Convolutional and Deep Learning based techniques for Time Series Ordinal Classificationcited by 1
  9. 2026In-Context Time Series Classification with Random Convolutional Featurescited by 1
  10. 2022Explainable classification of astronomical uncertain time seriescited by 0
  11. 2023ASTRIDE: Adaptive Symbolization for Time Series Databasescited by 0
  12. 2024Castor: Competing shapelets for fast and accurate time series classificationcited by 0
  13. 2024ECRTime: Ensemble Integration of Classification and Retrieval for Time Series Classificationcited by 0
  14. 2026Revisiting WEASEL 2.0: Reproduction, Sensitivity, and an Adaptive Ensemble-Size Rulecited by 0