# The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms. Extended Version

arXiv: https://arxiv.org/abs/1602.01711 · HTML: https://mlsanity.com/paper/1602.01711/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2016.

## Built on by (most cited first, 19 of 19)

- [Causal Inference for Time series Analysis: Problems, Methods and Evaluation](https://mlsanity.com/paper/2102.05829/) (2021)
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- [WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification](https://mlsanity.com/paper/2301.10194/) (2023)
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- [ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series](https://mlsanity.com/paper/2401.05381/) (2023)
- [Similarity Learning based Few Shot Learning for ECG Time Series Classification](https://mlsanity.com/paper/2202.00612/) (2022)
- [Forest Proximities for Time Series](https://mlsanity.com/paper/2410.03098/) (2024)
- [Rank Supervised Contrastive Learning for Time Series Classification](https://mlsanity.com/paper/2401.18057/) (2024)
- [Time-Series Forecasting in Smart Manufacturing Systems: An Experimental Evaluation of the State-of-the-art Algorithms](https://mlsanity.com/paper/2411.17499/) (2024)
- [Mimic: An adaptive algorithm for multivariate time series classification](https://mlsanity.com/paper/2111.04273/) (2021)
- [Automated Mobility Context Detection with Inertial Signals](https://mlsanity.com/paper/2205.08409/) (2022)
- [Back to Basics: A Sanity Check on Modern Time Series Classification Algorithms](https://mlsanity.com/paper/2308.07886/) (2023)
- [TCGAN: Convolutional Generative Adversarial Network for Time Series Classification and Clustering](https://mlsanity.com/paper/2309.04732/) (2023)
- [A Spatiotemporal Deep Neural Network for Fine-Grained Multi-Horizon Wind Prediction](https://mlsanity.com/paper/2309.04733/) (2023)
- [Swarm Characteristics Classification Using Neural Networks](https://mlsanity.com/paper/2403.19572/) (2024)
- [Time Elastic Neural Networks](https://mlsanity.com/paper/2405.17516/) (2024)
- [A time-series classification framework for individual-level absenteeism prediction under severe class imbalance](https://mlsanity.com/paper/2606.31532/) (2026)
- [A Heisenberg Lift Descriptor for Order Sensitive Online Handwriting Recognition](https://mlsanity.com/paper/2609.17565/) (2026)

## Abstract (arXiv)

In the last five years there have been a large number of new time series classification algorithms proposed in the literature. These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside time series classification archive. The archive has recently been expanded to 85 data sets, over half of which have been donated by researchers at the University of East Anglia. Aspects of previous evaluations have made comparisons between algorithms difficult. For example, several different programming languages have been used, experiments involved a single train/test split and some used normalised data whilst others did not. The relaunch of the archive provides a timely opportunity to thoroughly evaluate algorithms on a larger number of datasets. We have implemented 18 recently proposed algorithms in a common Java framework and compared them against two standard benchmark classifiers (and each other) by performing 100 resampling experiments on each of the 85 datasets. We use these results to test several hypotheses relating to whether the algorithms are significantly more accurate than the benchmarks and each other. Our results indicate that only 9 of these algorithms are significantly more accurate than both benchmarks and that one classifier, the Collective of Transformation Ensembles, is significantly more accurate than all of the others. All of our experiments and results are reproducible: we release all of our code, results and experimental details and we hope these experiments form the basis for more rigorous testing of new algorithms in the future.