Counterfactual Explanations for Multivariate Time Series
Open accessAbstract
Multivariate time series are used in many science and engineering domains, including health-care, astronomy, and high-performance computing. A recent trend is to use machine learning (ML) to process this complex data and these ML-based frameworks are starting to play a critical role for a variety of applications. However, barriers such as user distrust or difficulty of debugging need to be overcome to enable widespread adoption of such frameworks in production systems. To address this challenge, we propose a novel explainability technique,CoMTE, that providescounterfactualexplanationsforsupervisedmachinelearningframeworksonmultivariatetimeseriesdata. Using various machine learning frameworks and data sets, we compare CoMTE with several state-of-the-art explainability methods and show that we outperform existing methods in comprehensibility and robustness. We also show how CoMTE can be used to debug machine learning frameworks and gain a better understanding of the underlying multivariate time series data.
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Various techniques have been proposed to analyze longitudinal data with XAI, but most have not yet been extensively applied to real-world clinical questions.
“Similarly, various techniques have been proposed to analyze longitudinal data with XAI57,87–90, but most have not yet been extensively applied to real-world clinical questions.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [87]
The Introduction (p. 2) proposes a counterfactual explanation method for multivariate time-series data, supporting this paper as an example of a proposed XAI technique. The Data Sets section (p. 6) describes HPC telemetry and motion-classification data, not clinical applications. The paper does not establish the broader claim that most such techniques have not been extensively applied to real-world clinical questions. (LLM, conf=0.86)
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- Authors
- Ates, E., Aksar, B., Leung, V. J., & Coskun, A. K.
- Affiliations
- Boston University,Dept. of Electrical and Computer Eng.,Boston,MA,USA
- Boston University (US)
- Sandia National Laboratories (US)
- Venue
- 2021 International Conference on Applied Artificial Intelligence (ICAPAI)
- Publisher
- IEEE
- Published
- 2021-05-19
- Pages
- 1-8
- OpenAlex
- W3081435493
- Cited by
- 88
- References
- 69
- Citations by year
- 2026: 13 · 2025: 15 · 2024: 24 · 2023: 17 · 2022: 10 · 2021: 8 · 2020: 1
- FWCI
- 6.81
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Conference on Applied Artificial Intelligence (ICAPAI) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- counterfactual explanations, multivariate time series, explainable machine learning, Comte, model interpretability
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