# The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier

arXiv: https://arxiv.org/abs/2201.12048 · HTML: https://mlsanity.com/paper/2201.12048/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 28 Jan 2022.

## Builds on (most cited first, 16 of 16)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [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)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [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)
- [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)
- [The Canonical Interval Forest (CIF) Classifier for Time Series Classification](https://mlsanity.com/paper/2008.09172/) (2020)
- [The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification](https://mlsanity.com/paper/2105.03841/) (2021)
- [Scalable Dictionary Classifiers for Time Series Classification](https://mlsanity.com/paper/1907.11815/) (2019)
- [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)

## Built on by (most cited first, 18 of 18)

- [Bake off redux: a review and experimental evaluation of recent time series classification algorithms](https://mlsanity.com/paper/2304.13029/) (2023)
- [aeon: a Python toolkit for learning from time series](https://mlsanity.com/paper/2406.14231/) (2024)
- [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)
- [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)
- [Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion](https://mlsanity.com/paper/2308.01071/) (2023)
- [Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation](https://mlsanity.com/paper/2501.07999/) (2025)
- [A Contrastive Diffusion-based Network (CDNet) for Time Series Classification](https://mlsanity.com/paper/2507.21357/) (2025)
- [Label-Guided Imputation via Forest-Based Proximities for Improved Time Series Classification](https://mlsanity.com/paper/2509.22919/) (2025)
- [The Density of Cross-Persistence Diagrams and Its Applications](https://mlsanity.com/paper/2603.11623/) (2026)
- [In-Context Time Series Classification with Random Convolutional Features](https://mlsanity.com/paper/2607.19234/) (2026)
- [TS2TabPFN: Time Series Classification and Extrinsic Regression through Feature Extraction and a Tabular Foundation Model](https://mlsanity.com/paper/2608.04174/) (2026)
- [ECRTime: Ensemble Integration of Classification and Retrieval for Time Series Classification](https://mlsanity.com/paper/2407.14735/) (2024)
- [Utilizing Data Fingerprints for Privacy-Preserving Algorithm Selection in Time Series Classification: Performance and Uncertainty Estimation on Unseen Datasets](https://mlsanity.com/paper/2409.08636/) (2024)
- [An Efficient Transport-Based Dissimilarity Measure for Time Series Classification under Warping Distortions](https://mlsanity.com/paper/2505.05676/) (2025)
- [Learning Interpretable Differentiable Logic Networks for Time-Series Classification](https://mlsanity.com/paper/2508.17512/) (2025)
- [MRMS-Net and LMRMS-Net: Scalable Multi-Representation Multi-Scale Networks for Time Series Classification](https://mlsanity.com/paper/2603.19315/) (2026)

## Abstract (arXiv)

There have recently been significant advances in the accuracy of algorithms proposed for time series classification (TSC). However, a commonly asked question by real world practitioners and data scientists less familiar with the research topic, is whether the complexity of the algorithms considered state of the art is really necessary. Many times the first approach suggested is a simple pipeline of summary statistics or other time series feature extraction approaches such as TSFresh, which in itself is a sensible question; in publications on TSC algorithms generalised for multiple problem types, we rarely see these approaches considered or compared against. We experiment with basic feature extractors using vector based classifiers shown to be effective with continuous attributes in current state-of-the-art time series classifiers. We test these approaches on the UCR time series dataset archive, looking to see if TSC literature has overlooked the effectiveness of these approaches. We find that a pipeline of TSFresh followed by a rotation forest classifier, which we name FreshPRINCE, performs best. It is not state of the art, but it is significantly more accurate than nearest neighbour with dynamic time warping, and represents a reasonable benchmark for future comparison.