Time series forecasting enables early warning and has driven asset performance management from traditional planned maintenance to predictive maintenance. However, the lack of interpretability in forecasting methods undermines users' trust and complicates debugging for developers. Consequently, interpretable time-series forecasting has attracted increasing research attention. Nevertheless, existing methods suffer from several limitations, including insufficient modeling of temporal dependencies, lack of feature-level interpretability to support early warning, and difficulty in simultaneously achieving the accuracy and interpretability. This paper proposes the interpretable polynomial learning (IPL) method, which integrates interpretability into the model structure by explicitly modeling original features and their interactions of arbitrary order through polynomial representations. This design preserves temporal dependencies, provides feature-level interpretability, and offers a flexible trade-off between prediction accuracy and interpretability by adjusting the polynomial degree. We evaluate IPL on simulated and Bitcoin price data, showing that it achieves high prediction accuracy with superior interpretability compared with widely used explainability methods. Experiments on field-collected antenna data further demonstrate that IPL yields simpler and more efficient early warning mechanisms.

翻译：时间序列预测能够实现早期预警，并推动资产性能管理从传统计划性维护转向预测性维护。然而，预测方法缺乏可解释性，削弱了用户的信任，并使开发者的调试工作复杂化。因此，可解释的时间序列预测已吸引了越来越多的研究关注。然而，现有方法存在若干局限性，包括对时间依赖关系建模不足、缺乏支持早期预警的特征级可解释性，以及难以同时实现准确性与可解释性。本文提出了可解释多项式学习方法，该方法通过多项式表示显式建模原始特征及其任意阶交互，从而将可解释性融入模型结构。该设计保留了时间依赖性，提供了特征级可解释性，并通过调整多项式阶数为预测准确性与可解释性之间提供了灵活的权衡。我们在模拟数据和比特币价格数据上评估了IPL，结果表明，与广泛使用的可解释性方法相比，IPL在实现高预测精度的同时，具有更优的可解释性。在实地采集的天线数据上进行的实验进一步证明，IPL能产生更简单、更高效的早期预警机制。