# Bake off redux: a review and experimental evaluation of recent time series classification algorithms

arXiv: https://arxiv.org/abs/2304.13029 · HTML: https://mlsanity.com/paper/2304.13029/

## Key facts

- Cited by 101 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 91 references that are in the map.
- First posted on arXiv 25 Apr 2023.

## Builds on (most cited first, 30 of 31)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [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)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://mlsanity.com/paper/2012.08791/) (2020)
- [Benchmarking Multivariate Time Series Classification Algorithms](https://mlsanity.com/paper/2007.13156/) (2020)
- [HIVE-COTE 2.0: a new meta ensemble for time series classification](https://mlsanity.com/paper/2104.07551/) (2021)
- [Should we really use post-hoc tests based on mean-ranks?](https://mlsanity.com/paper/1505.02288/) (2015)
- [catch22: CAnonical Time-series CHaracteristics](https://mlsanity.com/paper/1901.10200/) (2019)
- [MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification](https://mlsanity.com/paper/2102.00457/) (2021)
- [A Time Series Forest for Classification and Feature Extraction](https://mlsanity.com/paper/1302.2277/) (2013)
- [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://mlsanity.com/paper/2302.02515/) (2023)
- [TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification](https://mlsanity.com/paper/1906.10329/) (2019)
- [HYDRA: Competing convolutional kernels for fast and accurate time series classification](https://mlsanity.com/paper/2203.13652/) (2022)
- [The Canonical Interval Forest (CIF) Classifier for Time Series Classification](https://mlsanity.com/paper/2008.09172/) (2020)
- [Fast and Accurate Time Series Classification with WEASEL](https://mlsanity.com/paper/1701.07681/) (2017)
- [Proximity Forest: An effective and scalable distance-based classifier for time series](https://mlsanity.com/paper/1808.10594/) (2018)
- [A review on distance based time series classification](https://mlsanity.com/paper/1806.04509/) (2018)
- [shapeDTW: shape Dynamic Time Warping](https://mlsanity.com/paper/1606.01601/) (2016)
- [Interpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic Representations](https://mlsanity.com/paper/2006.01667/) (2020)
- [The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification](https://mlsanity.com/paper/2105.03841/) (2021)
- [Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets](https://mlsanity.com/paper/2109.13514/) (2021)
- [Scalable Dictionary Classifiers for Time Series Classification](https://mlsanity.com/paper/1907.11815/) (2019)
- [The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier](https://mlsanity.com/paper/2201.12048/) (2022)
- [From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers](https://mlsanity.com/paper/1809.06751/) (2018)
- [A Review and Evaluation of Elastic Distance Functions for Time Series Clustering](https://mlsanity.com/paper/2205.15181/) (2022)
- [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)
- [Is rotation forest the best classifier for problems with continuous features?](https://mlsanity.com/paper/1809.06705/) (2018)
- [Fast, Accurate and Interpretable Time Series Classification Through Randomization](https://mlsanity.com/paper/2105.14876/) (2021)

## Built on by (most cited first, 30 of 101)

- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [UniTS: A Unified Multi-Task Time Series Model](https://mlsanity.com/paper/2403.00131/) (2024)
- [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)
- [ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis](https://mlsanity.com/paper/2403.01493/) (2024)
- [Inherently Interpretable Time Series Classification via Multiple Instance Learning](https://mlsanity.com/paper/2311.10049/) (2023)
- [QUANT: A Minimalist Interval Method for Time Series Classification](https://mlsanity.com/paper/2308.00928/) (2023)
- [Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach](https://mlsanity.com/paper/2505.20446/) (2025)
- [Cross-Domain Pre-training with Language Models for Transferable Time Series Representations](https://mlsanity.com/paper/2403.12372/) (2024)
- [Advancing Time Series Classification with Multimodal Language Modeling](https://mlsanity.com/paper/2403.12371/) (2024)
- [Learning Soft Sparse Shapes for Efficient Time-Series Classification](https://mlsanity.com/paper/2505.06892/) (2025)
- [MAESTRO : Adaptive Sparse Attention and Robust Learning for Multimodal Dynamic Time Series](https://mlsanity.com/paper/2509.25278/) (2025)
- [Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression](https://mlsanity.com/paper/2305.01429/) (2023)
- [Look Into the LITE in Deep Learning for Time Series Classification](https://mlsanity.com/paper/2409.02869/) (2024)
- [Pre-trained Forecasting Models: Strong Zero-Shot Feature Extractors for Time Series Classification](https://mlsanity.com/paper/2510.26777/) (2025)
- [Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels](https://mlsanity.com/paper/2309.14518/) (2023)
- [Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fields](https://mlsanity.com/paper/2311.18029/) (2023)
- [Exploring Kolmogorov-Arnold Networks for Interpretable Time Series Classification](https://mlsanity.com/paper/2411.14904/) (2024)
- [On the Feasibility of Vision-Language Models for Time-Series Classification](https://mlsanity.com/paper/2412.17304/) (2024)
- [Robust Explainer Recommendation for Time Series Classification](https://mlsanity.com/paper/2306.05501/) (2023)
- [QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models -- Extended Version](https://mlsanity.com/paper/2404.13990/) (2024)
- [MONSTER: Monash Scalable Time Series Evaluation Repository](https://mlsanity.com/paper/2502.15122/) (2025)
- [Phase-driven Domain Generalizable Learning for Nonstationary Time Series](https://mlsanity.com/paper/2402.05960/) (2024)
- [DeNOTS: Stable Deep Neural ODEs for Time Series](https://mlsanity.com/paper/2408.08055/) (2024)
- [MASCOTS: Model-Agnostic Symbolic COunterfactual explanations for Time Series](https://mlsanity.com/paper/2503.22389/) (2025)
- [Finding Foundation Models for Time Series Classification with a PreText Task](https://mlsanity.com/paper/2311.14534/) (2023)
- [Early Classification of Time Series: A Survey and Benchmark](https://mlsanity.com/paper/2406.18332/) (2024)
- [LLM-ABBA: Understanding time series via symbolic approximation](https://mlsanity.com/paper/2411.18506/) (2024)
- [The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification](https://mlsanity.com/paper/2603.20352/) (2026)
- [RocketPFN: Accurate Time Series Classification via In-Context Learning](https://mlsanity.com/paper/2606.21786/) (2026)

## Abstract (arXiv)

In 2017, a research paper compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the University of California, Riverside (UCR) archive. This study, commonly referred to as a `bake off', identified that only nine algorithms performed significantly better than the Dynamic Time Warping (DTW) and Rotation Forest benchmarks that were used. The study categorised each algorithm by the type of feature they extract from time series data, forming a taxonomy of five main algorithm types. This categorisation of algorithms alongside the provision of code and accessible results for reproducibility has helped fuel an increase in popularity of the TSC field. Over six years have passed since this bake off, the UCR archive has expanded to 112 datasets and there have been a large number of new algorithms proposed. We revisit the bake off, seeing how each of the proposed categories have advanced since the original publication, and evaluate the performance of newer algorithms against the previous best-of-category using an expanded UCR archive. We extend the taxonomy to include three new categories to reflect recent developments. Alongside the originally proposed distance, interval, shapelet, dictionary and hybrid based algorithms, we compare newer convolution and feature based algorithms as well as deep learning approaches. We introduce 30 classification datasets either recently donated to the archive or reformatted to the TSC format, and use these to further evaluate the best performing algorithm from each category. Overall, we find that two recently proposed algorithms, Hydra+MultiROCKET and HIVE-COTEv2, perform significantly better than other approaches on both the current and new TSC problems.