mlsanityData to 14 Oct 2026

XAI for time-series classification leveraging image highlight methods

arXiv 2311.17110 ↗First posted 28 Nov 2023cs.LG cs.AIcited by 2

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Abstract (arXiv)

Although much work has been done on explainability in the computer vision and natural language processing (NLP) fields, there is still much work to be done to explain methods applied to time series as time series by nature can not be understood at first sight. In this paper, we present a Deep Neural Network (DNN) in a teacher-student architecture (distillation model) that offers interpretability in time-series classification tasks. The explainability of our approach is based on transforming the time series to 2D plots and applying image highlight methods (such as LIME and GradCam), making the predictions interpretable. At the same time, the proposed approach offers increased accuracy competing with the baseline model with the trade-off of increasing the training time.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 33 references that are in the map. First posted on arXiv 28 Nov 2023.

Builds on

20 of 33 references.

References · 20

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8 more
  1. 2021Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Surveycited by 72
  2. 2016Graying the black box: Understanding DQNscited by 68
  3. 2019DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systemscited by 59
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024Time is Not Enough: Time-Frequency based Explanation for Time-Series Black-Box Modelscited by 6
  2. 2026Persistent and Conversational Multi-Method Explainability for Trustworthy Financial AIcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Enhancing deep learning models for time series classification via knowledge distillation
  2. 2026EXCODER: EXplainable Classification Of DiscretE time series Representations
  3. 2024On the Feasibility of Vision-Language Models for Time-Series Classification
  4. 2025Harnessing Vision Models for Time Series Analysis: A Survey
  5. 2022Rethinking Attention Mechanism in Time Series Classification
  6. 2024A Comprehensive Survey of Deep Learning for Time Series Forecasting: Architectural Diversity and Open Challenges
  7. 2024Advancing Time Series Classification with Multimodal Language Modeling
  8. 2025A Contrastive Diffusion-based Network (CDNet) for Time Series Classification