arXiv AI
Software Frameworks for Explainable AI in Time Series Classification: A Systematic Review
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Using LLMs for Explainable, Data-Driven Insight Generation from Time Series
arXiv:2607. 18271v1 Announce Type: new Abstract: Time series forecasts are widely used in decision-critical domains, where they are rarely consumed without accompanying explanations.
arXiv AI
Sep 28
A Synthetic Ground-Truth Framework for the Evaluation of Explainable AI Methods
The paper introduces a synthetic ground‑truth framework for evaluating explainable AI (XAI) methods, addressing the lack of reliable evaluation procedures. By using controlled interventions to create datasets where the importance of input components is known, the framework generates ground‑truth explanations that align with the model’s actual decision process. The authors apply this approach to binary images, tabular data, and time series, and find that nine popular XAI methods exhibit significant limitations, underscoring the need for intervention‑based benchmarks.
arXiv Machine Learning
Aug 27
Towards A Unified Information Bottleneck Framework for Time Series Explanations
arXiv:2608. 25897v1 Announce Type: new Abstract: Explaining deep learning models operating on time series data is crucial in various applications that require transparent and interpretable insights into model behavior.
Hugging Face Trending Papers
Aug 26
Towards A Unified Information Bottleneck Framework for Time Series Explanations
Explaining deep learning models operating on time series data is crucial in various applications that require transparent and interpretable insights into model behavior. {Existing explanation methods...
arXiv AI
Jul 21
L2GTX: From Local to Global Time Series Explanations
arXiv:2603. 13065v2 Announce Type: replace-cross Abstract: Deep learning models achieve high accuracy in time series classification, yet understanding their class-level decision behaviour remains challenging.
arXiv AI
Jun 30
When, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregate
arXiv:2512. 03578v3 Announce Type: replace-cross Abstract: Time series extrinsic regression (TSER) refers to the task of predicting a continuous target variable from an input time series.