mlsanityData to 7 Oct 2026

XAI Methods for Neural Time Series Classification: A Brief Review

arXiv 2108.08009 ↗First posted 18 Aug 2021cs.LG cs.AIcited by 7

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Abstract (arXiv)

Deep learning models have recently demonstrated remarkable results in a variety of tasks, which is why they are being increasingly applied in high-stake domains, such as industry, medicine, and finance. Considering that automatic predictions in these domains might have a substantial impact on the well-being of a person, as well as considerable financial and legal consequences to an individual or a company, all actions and decisions that result from applying these models have to be accountable. Given that a substantial amount of data that is collected in high-stake domains are in the form of time series, in this paper we examine the current state of eXplainable AI (XAI) methods with a focus on approaches for opening up deep learning black boxes for the task of time series classification. Finally, our contribution also aims at deriving promising directions for future work, to advance XAI for deep learning on time series data.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 20 of its 35 references that are in the map. First posted on arXiv 18 Aug 2021.

Builds on

20 of 35 references.

References · 20

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8 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 1; 2025: 1; 2026: 1.

7 papers.

  1. 2023Inherently Interpretable Time Series Classification via Multiple Instance Learningcited by 23
  2. 2023Unsupervised Representation Learning for Time Series: A Reviewcited by 14
  3. 2024Explainable Artificial Intelligence for Medical Applications: A Reviewcited by 12
  4. 2021Interpretable Feature Learning Framework for Smoking Behavior Detectioncited by 0
  5. 2023Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Seriescited by 0
  6. 2025Learning Temporal Saliency for Time Series Forecasting with Cross-Scale Attentioncited by 0
  7. 2026Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attackscited by 0