mlsanityData to 7 Oct 2026

TSInterpret: A unified framework for time series interpretability

arXiv 2208.05280 ↗First posted 10 Aug 2022cs.LGcited by 3

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Abstract (arXiv)

With the increasing application of deep learning algorithms to time series classification, especially in high-stake scenarios, the relevance of interpreting those algorithms becomes key. Although research in time series interpretability has grown, accessibility for practitioners is still an obstacle. Interpretability approaches and their visualizations are diverse in use without a unified API or framework. To close this gap, we introduce TSInterpret an easily extensible open-source Python library for interpreting predictions of time series classifiers that combines existing interpretation approaches into one unified framework. The library features (i) state-of-the-art interpretability algorithms, (ii) exposes a unified API enabling users to work with explanations consistently and provides (iii) suitable visualizations for each explanation.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 22 of its 41 references that are in the map. First posted on arXiv 10 Aug 2022.

Builds on

22 of 41 references.

References · 22

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 612
  4. 2018Deep learning for time series classification: a reviewcited by 575
  5. 2020Captum: A unified and generic model interpretability library for PyTorchcited by 542
  6. 2018The UCR Time Series Archivecited by 421
  7. 2018The UEA multivariate time series classification archive, 2018cited by 330
  8. 2018Scalable and accurate deep learning for electronic health recordscited by 275
  9. 2019Understanding Deep Networks via Extremal Perturbations and Smooth Maskscited by 257
  10. 2019InterpretML: A Unified Framework for Machine Learning Interpretabilitycited by 243
  11. 2019The What-If Tool: Interactive Probing of Machine Learning Modelscited by 207
  12. 2019One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniquescited by 164
10 more
  1. 2020Benchmarking Multivariate Time Series Classification Algorithmscited by 145
  2. 2020Benchmarking Deep Learning Interpretability in Time Series Predictionscited by 102
  3. 2021CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithmscited by 85
  4. 2021Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Surveycited by 71
  5. 2020Instance-based Counterfactual Explanations for Time Series Classificationcited by 64
  6. 2018iNNvestigate neural networks!cited by 58
  7. 2020dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Pythoncited by 40
  8. 2018TSViz: Demystification of Deep Learning Models for Time-Series Analysiscited by 25
  9. 2022OmniXAI: A Library for Explainable AIcited by 21
  10. 2020TSInsight: A local-global attribution framework for interpretability in time-series datacited by 8

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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 2.

3 papers.

  1. 2023Time Interpret: a Unified Model Interpretability Library for Time Seriescited by 3
  2. 2025Counterfactual Explanations for Time Series Should be Human-Centered and Temporally Coherent in Interventionscited by 2
  3. 2025Guided by Stars: Interpretable Concept Learning Over Time Series via Temporal Logic Semanticscited by 0