基于注意力的经验回放框架用于不可知时间序列预测模型的持续学习
Attention-based Experience Replay Framework for Continual Learning of Agnostic Time Series Forecasting Models
AI总结：
针对神经网络依赖固定数据集无法适应动态环境的问题，提出基于注意力机制引导经验回放策略的持续时间序列预测框架，能动态适应新环境保留知识，减轻遗忘，在多数据集上评估显示可提高预测性能、降低成本和数据需求。
AI中文摘要：
深度学习在人工智能领域取得了显著进展，但神经网络严重依赖大型固定数据集，在数据分布随时间变化的动态环境中难以满足。持续学习旨在应对这一挑战。本文引入一种新颖的持续时间序列预测框架，通过注意力机制引导的经验回放策略扩展现有静态预测模型。该方法能让模型动态适应新环境并保留先验知识，减轻灾难性遗忘。在标准预测基准和具有不同时间行为的测压数据集上评估，结果表明该方法有效提高或维持预测性能，降低再训练成本和数据需求，便于在动态和现实环境中部署预测模型。
英文摘要：
Deep learning has led to remarkable progress in artificial intelligence, particularly in robotics, imaging and sound processing. However, a major limitation of neural networks remains their strong dependence on large and stationary datasets. In many real-world applications, these conditions are rarely met due to evolving and dynamic environments where data distributions change over time. Continual learning aims to address this challenge by developing models capable of adapting incrementally while maintaining a balance between stability and plasticity under computational constraints. In this work, we introduce a novel framework for continual time series forecasting, designed to extend existing static forecasting models commonly used in the literature by incorporating an Experience Replay strategy guided by Attention mechanisms. This approach allows the model to adapt dynamically to new contexts while preserving prior knowledge, effectively mitigating catastrophic forgetting. The framework is evaluated on standard forecasting benchmarks as well as on a piezometric dataset exhibiting diverse temporal behaviors. Results show that our approach effectively increases or maintains predictive performance over time while reducing retraining costs and data requirements, thus facilitating the deployment of forecasting models in dynamic and real-world settings.
