AI 中文总结
研究时间序列预测持续学习中的可解释性挑战，利用经验回放策略，通过神经预测架构和基于注意力的采样机制，借助注意力展开和梯度归因方法分析行为，实验表明可揭示归因模式演变及为非平稳预测提供策略参考。
AI 中文摘要
深度学习模型在时间序列预测中展现出强大潜力，但由于非平稳动态和有限的可解释性，在实际环境监测中的部署仍具挑战性。本文利用经验回放策略，将可解释性作为理解自适应时间序列持续学习的核心工具进行研究。研究神经预测架构并通过基于注意力的采样机制增强以支持模型随时间适应，利用注意力展开和基于梯度的归因方法分析预测行为和采样策略。对具有异质模式和状态转移的实际测压时间序列进行实验表明，分析模型和采样行为能为持续学习框架动态提供有价值见解，揭示归因模式如何随时间演变以及如何为非平稳预测场景中的数据选择和适应策略提供信息。
英文摘要
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.
Journal refEDBT/ICDT 2026 Joint Conference, Mar 2026, Tampere, Finland
