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.

翻译：大多数机器学习方法假设概率分布固定，这限制了它们在非平稳现实场景中的适用性。虽然持续学习方法解决了这一问题，但当前方法通常依赖黑盒模型或需要大量用户干预以实现可解释性。我们提出SyMPLER（基于分段线性演化回归的系统建模方法），这是一种基于动态分段线性近似的、用于非平稳环境下时间序列预测的可解释模型。与其他局部线性模型不同，SyMPLER利用统计学习理论中的泛化界，根据预测误差自动确定何时添加新的局部模型，从而无需对数据进行显式聚类。实验表明，SyMPLER能够达到与黑盒模型及现有可解释模型相当的性能，同时保持人类可解释的结构，从而揭示系统行为的深层洞察。在此意义上，我们的方法协调了准确性与可解释性，为非平稳时间序列预测提供了一种透明且自适应的解决方案。