用理论指导更新，让时间序列模型自动适应变化且可解释。
Locally Linear Continual Learning for Time Series based on VC-Theoretical Generalization Bounds
- 基于统计学习理论的泛化界，自动判断何时新增局部线性模型。
- 在非平稳数据上性能媲美黑箱模型，且保持结构可读。
- 适合需要透明决策过程的工业预测场景。
多数机器学习方法假设概率分布固定，限制了其在非平稳现实场景中的应用。尽管持续学习方法解决了此问题，但现有方法常依赖黑箱模型或需大量人工干预以保证可解释性。我们提出SyMPLER（基于分段线性演化回归的系统建模），一种面向非平稳环境的时间序列预测可解释模型，采用动态分段线性逼近。与其它局部线性模型不同，SyMPLER利用统计学习理论中的泛化界，根据预测误差自动决定是否添加新局部模型，无需显式数据聚类。实验表明，SyMPLER在性能上可媲美黑箱模型及现有可解释模型，同时保持人类可读结构，揭示系统行为规律。本方法兼顾准确率与可解释性，为非平稳时间序列预测提供透明且自适应的解决方案。
原文摘要 · Abstract (English)
Most machine learning methods assume fixed probability distributions, limiting their applicability in nonstationary real-world scenarios. While continual learning methods address this issue, current approaches often rely on black-box models or require extensive user intervention for interpretability. We propose SyMPLER (Systems Modeling through Piecewise Linear Evolving Regression), an explainable model for time series forecasting in nonstationary environments based on dynamic piecewise-linear approximations. Unlike other locally linear models, SyMPLER uses generalization bounds from Statistical Learning Theory to automatically determine when to add new local models based on prediction errors, eliminating the need for explicit clustering of the data. Experiments show that SyMPLER can achieve comparable performance to both black-box and existing explainable models while maintaining a human-interpretable structure that reveals insights about the system's behavior. In this sense, our approach conciliates accuracy and interpretability, offering a transparent and adaptive solution for forecasting nonstationary time series.
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