mlsanityData to 7 Oct 2026

Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression

arXiv 2305.01429 ↗First posted 2 May 2023cs.LG stat.MLcited by 9

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Abstract (arXiv)

Time Series Extrinsic Regression (TSER) involves using a set of training time series to form a predictive model of a continuous response variable that is not directly related to the regressor series. The TSER archive for comparing algorithms was released in 2022 with 19 problems. We increase the size of this archive to 63 problems and reproduce the previous comparison of baseline algorithms. We then extend the comparison to include a wider range of standard regressors and the latest versions of TSER models used in the previous study. We show that none of the previously evaluated regressors can outperform a regression adaptation of a standard classifier, rotation forest. We introduce two new TSER algorithms developed from related work in time series classification. FreshPRINCE is a pipeline estimator consisting of a transform into a wide range of summary features followed by a rotation forest regressor. DrCIF is a tree ensemble that creates features from summary statistics over random intervals. Our study demonstrates that both algorithms, along with InceptionTime, exhibit significantly better performance compared to the other 18 regressors tested. More importantly, these two proposals (DrCIF and FreshPRINCE) models are the only ones that significantly outperform the standard rotation forest regressor.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 21 of its 83 references that are in the map. First posted on arXiv 2 May 2023.

Builds on

21 of 83 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  3. 2021Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layerscited by 607
  4. 2018Deep learning for time series classification: a reviewcited by 575
  5. 2019InceptionTime: Finding AlexNet for Time Series Classificationcited by 386
  6. 2016Time Series Classification from Scratch with Deep Neural Networks: A Strong Baselinecited by 358
  7. 2019ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernelscited by 336
  8. 2020Benchmarking Multivariate Time Series Classification Algorithmscited by 145
  9. 2022Liquid Structural State-Space Modelscited by 134
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  11. 2015Should we really use post-hoc tests based on mean-ranks?cited by 116
  12. 2019catch22: CAnonical Time-series CHaracteristicscited by 103
9 more
  1. 2023Bake off redux: a review and experimental evaluation of recent time series classification algorithmscited by 99
  2. 2021MultiRocket: Multiple pooling operators and transformations for fast and effective time series classificationcited by 89
  3. 2013A Time Series Forest for Classification and Feature Extractioncited by 69
  4. 2020The Canonical Interval Forest (CIF) Classifier for Time Series Classificationcited by 48
  5. 2021The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classificationcited by 26
  6. 2022The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifiercited by 18
  7. 2022Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Datacited by 15
  8. 2018Is rotation forest the best classifier for problems with continuous features?cited by 11
  9. 2016Online Decorrelation of Humidity and Temperature in Chemical Sensors for Continuous Monitoringcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 1; 2026: 3.

9 papers.

  1. 2024aeon: a Python toolkit for learning from time seriescited by 46
  2. 2023An Approach to Multiple Comparison Benchmark Evaluations that is Stable Under Manipulation of the Comparate Setcited by 27
  3. 2023Finding Foundation Models for Time Series Classification with a PreText Taskcited by 4
  4. 2024LLM-ABBA: Understanding time series via symbolic approximationcited by 4
  5. 2026The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classificationcited by 3
  6. 2023Convolutional and Deep Learning based techniques for Time Series Ordinal Classificationcited by 1
  7. 2026TS2TabPFN: Time Series Classification and Extrinsic Regression through Feature Extraction and a Tabular Foundation Modelcited by 1
  8. 2025When, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregatecited by 0
  9. 2026Statistical comparisons of time-series feature sets on classification taskscited by 0