# The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification arXiv: https://arxiv.org/abs/2105.03841 ยท HTML: https://mlsanity.com/paper/2105.03841/ ## 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 (most cited first, 10 of 10) - [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018) - [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019) - [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019) - [Should we really use post-hoc tests based on mean-ranks?](https://mlsanity.com/paper/1505.02288/) (2015) - [A Time Series Forest for Classification and Feature Extraction](https://mlsanity.com/paper/1302.2277/) (2013) - [TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification](https://mlsanity.com/paper/1906.10329/) (2019) - [Fast and Accurate Time Series Classification with WEASEL](https://mlsanity.com/paper/1701.07681/) (2017) - [Scalable Dictionary Classifiers for Time Series Classification](https://mlsanity.com/paper/1907.11815/) (2019) - [From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers](https://mlsanity.com/paper/1809.06751/) (2018) - [A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0](https://mlsanity.com/paper/2004.06069/) (2020) ## Built on by (most cited first, 26 of 26) - [Bake off redux: a review and experimental evaluation of recent time series classification algorithms](https://mlsanity.com/paper/2304.13029/) (2023) - [HYDRA: Competing convolutional kernels for fast and accurate time series classification](https://mlsanity.com/paper/2203.13652/) (2022) - [aeon: a Python toolkit for learning from time series](https://mlsanity.com/paper/2406.14231/) (2024) - [An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Set](https://mlsanity.com/paper/2305.11921/) (2023) - [Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets](https://mlsanity.com/paper/2109.13514/) (2021) - [NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining](https://mlsanity.com/paper/2310.07402/) (2023) - [The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier](https://mlsanity.com/paper/2201.12048/) (2022) - [WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification](https://mlsanity.com/paper/2301.10194/) (2023) - [QUANT: A Minimalist Interval Method for Time Series Classification](https://mlsanity.com/paper/2308.00928/) (2023) - [Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression](https://mlsanity.com/paper/2305.01429/) (2023) - [Neuro-symbolic Models for Interpretable Time Series Classification using Temporal Logic Description](https://mlsanity.com/paper/2209.09114/) (2022) - [Fast, Accurate and Interpretable Time Series Classification Through Randomization](https://mlsanity.com/paper/2105.14876/) (2021) - [Proximity Forest 2.0: A new effective and scalable similarity-based classifier for time series](https://mlsanity.com/paper/2304.05800/) (2023) - [POCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspective](https://mlsanity.com/paper/2309.08499/) (2023) - [The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification](https://mlsanity.com/paper/2603.20352/) (2026) - [Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion](https://mlsanity.com/paper/2308.01071/) (2023) - [Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning Infusion](https://mlsanity.com/paper/2302.06683/) (2023) - [FedST: Secure Federated Shapelet Transformation for Time Series Classification](https://mlsanity.com/paper/2302.10631/) (2023) - [A Dictionary-based approach to Time Series Ordinal Classification](https://mlsanity.com/paper/2305.09288/) (2023) - [Convolutional and Deep Learning based techniques for Time Series Ordinal Classification](https://mlsanity.com/paper/2306.10084/) (2023) - [In-Context Time Series Classification with Random Convolutional Features](https://mlsanity.com/paper/2607.19234/) (2026) - [Explainable classification of astronomical uncertain time series](https://mlsanity.com/paper/2210.00869/) (2022) - [ASTRIDE: Adaptive Symbolization for Time Series Databases](https://mlsanity.com/paper/2302.04097/) (2023) - [Castor: Competing shapelets for fast and accurate time series classification](https://mlsanity.com/paper/2403.13176/) (2024) - [ECRTime: Ensemble Integration of Classification and Retrieval for Time Series Classification](https://mlsanity.com/paper/2407.14735/) (2024) - [Revisiting WEASEL 2.0: Reproduction, Sensitivity, and an Adaptive Ensemble-Size Rule](https://mlsanity.com/paper/2608.18021/) (2026) ## 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.