[Submitted on 1 Jul 2026]
Title:Challenges of Explainability in Continual Learning for Time Series Forecasting
View a PDF of the paper titled Challenges of Explainability in Continual Learning for Time Series Forecasting, by Quentin Besnard (RFAI) and 5 other authors
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Abstract:Deep learning models have shown strong potential for time series forecasting, yet their deployment in real-world environmental monitoring remains challenging due to non-stationary dynamics and limited explainability. In this work, we investigate explainability as a central tool for understanding continual learning in adaptive time series forecasting, with Experience Replay strategies. We study neural forecasting architectures such as PatchMixer, PatchTST and DLinear, augmented with attention-based sampling mechanisms to support model adaptation over time. Explainability is leveraged through attention rollout and gradient-based attribution methods (Grad-CAM) to analyze both predictive behavior and sampling strategies within a continual learning framework. Experiments conducted on real-world piezometric time series exhibiting heterogeneous patterns and regime shifts show that analyzing model and sampling behaviors provides valuable insights into the dynamics of the continual learning framework. Beyond predictive performance, our results highlight the challenges and opportunities of using explainability to understand continual learning behaviors, revealing how attribution patterns evolve over time and how they can inform data selection and adaptation strategies in non-stationary forecasting scenarios.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.19382 [cs.LG]
(or arXiv:2607.19382v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.19382
arXiv-issued DOI via DataCite (pending registration)
Journal reference: EDBT/ICDT 2026 Joint Conference, Mar 2026, Tampere, Finland
Submission history
From: Quentin Besnard [view email] [via CCSD proxy] [v1] Wed, 1 Jul 2026 08:35:26 UTC (5,352 KB)
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