Challenges of Explainability in Continual Learning for Time Series Forecasting
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 beha...
| Authors | Quentin Besnard, Emmanuel Doumard, Nicolas Labroche et al. |
|---|---|
| Primary category | cs.LG |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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