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sktime: A Unified Interface for Machine Learning with Time Series

arXiv 1909.07872 ↗First posted 17 Sep 2019cs.LG stat.MLcited by 102

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Abstract (arXiv)

We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series. Time series data gives rise to various distinct but closely related learning tasks, such as forecasting and time series classification, many of which can be solved by reducing them to related simpler tasks. We discuss the main rationale for creating a unified interface, including reduction, as well as the design of sktime's core API, supported by a clear overview of common time series tasks and reduction approaches.

Key facts. Cited by 102 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Sep 2019.

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Citing papers per year: 2021: 12; 2022: 15; 2023: 21; 2024: 16; 2025: 21; 2026: 17.

102 papers.

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  1. 2021Monash Time Series Forecasting Archivecited by 267
  2. 2021Darts: User-Friendly Modern Machine Learning for Time Seriescited by 91
  3. 2021Chaos as an interpretable benchmark for forecasting and data-driven modellingcited by 88
  4. 2025fev-bench: A Realistic Benchmark for Time Series Forecastingcited by 64
  5. 2024Deep Learning for Multivariate Time Series Imputation: A Surveycited by 57
  6. 2024aeon: a Python toolkit for learning from time seriescited by 47
  7. 2021Deeptime: a Python library for machine learning dynamical models from time series datacited by 38
  8. 2023ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizonscited by 36
  9. 2024Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Datacited by 36
  10. 2022DEPTS: Deep Expansion Learning for Periodic Time Series Forecastingcited by 29
  11. 2023Model scale versus domain knowledge in statistical forecasting of chaotic systemscited by 27
  12. 2022Review of automated time series forecasting pipelinescited by 18
38 more
  1. 2021AutoAI-TS: AutoAI for Time Series Forecastingcited by 15
  2. 2023WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classificationcited by 15
  3. 2022A Review and Evaluation of Elastic Distance Functions for Time Series Clusteringcited by 14
  4. 2023Clairvoyance: A Pipeline Toolkit for Medical Time Seriescited by 12
  5. 2024TSCMamba: Mamba Meets Multi-View Learning for Time Series Classificationcited by 10
  6. 2022Shapelet-Based Counterfactual Explanations for Multivariate Time Seriescited by 9
  7. 2021Elastic Similarity and Distance Measures for Multivariate Time Seriescited by 8
  8. 2022Scalable Classifier-Agnostic Channel Selection for Multivariate Time Series Classificationcited by 8
  9. 2022Fast and Robust Video-Based Exercise Classification via Body Pose Tracking and Scalable Multivariate Time Series Classifierscited by 8
  10. 2023Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernelscited by 8
  11. 2023Fast, Interpretable, and Deterministic Time Series Classification With a Bag-of-Receptive-Fieldscited by 8
  12. 2022HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Pythoncited by 7
  13. 2023Appliance Detection Using Very Low-Frequency Smart Meter Time Seriescited by 7
  14. 2023Time Series Clustering With Random Convolutional Kernelscited by 6
  15. 2021LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Valuescited by 5
  16. 2023TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicinecited by 5
  17. 2023Recurrences reveal shared causal drivers of complex time seriescited by 5
  18. 2021SentimentArcs: A Novel Method for Self-Supervised Sentiment Analysis of Time Series Shows SOTA Transformers Can Struggle Finding Narrative Arcscited by 4
  19. 2023UniTS: A Universal Time Series Analysis Framework Powered by Self-Supervised Representation Learningcited by 4
  20. 2024DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysiscited by 4
  21. 2024MEDS-Tab: Automated tabularization and baseline methods for MEDS datasetscited by 4
  22. 2026TempusBench: An Evaluation Framework for Time-Series Forecastingcited by 4
  23. 2021Machine learning based automated identification of thunderstorms from anemometric records using shapelet transformcited by 3
  24. 2022Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveScited by 3
  25. 2022Greedy Modality Selection via Approximate Submodular Maximizationcited by 3
  26. 2023Parameterizing the cost function of Dynamic Time Warping with application to time series classificationcited by 3
  27. 2023Clustering Method for Time-Series Images Using Quantum-Inspired Computing Technologycited by 3
  28. 2023An Examination of Wearable Sensors and Video Data Capture for Human Exercise Classificationcited by 3
  29. 2023Automatic Feature Engineering for Time Series Classification: Evaluation and Discussioncited by 3
  30. 2024Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning modelscited by 3
  31. 2021Designing Machine Learning Toolboxes: Concepts, Principles and Patternscited by 2
  32. 2021FDApy: a Python package for functional datacited by 2
  33. 2022Benchmark time series data sets for PyTorch -- the torchtime packagecited by 2
  34. 2024Motion Code: Robust Time Series Classification and Forecasting via Sparse Variational Multi-Stochastic Processes Learningcited by 2
  35. 2025TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor Learningcited by 2
  36. 2025VCDiag: Classifying Erroneous Waveforms for Failure Triage Accelerationcited by 2
  37. 2025MSAD: A Deep Dive into Model Selection for Time series Anomaly Detectioncited by 2
  38. 2025FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classificationcited by 2

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