As time evolves, data within specific domains exhibit predictability that motivates time series forecasting to predict future trends from historical data. However, current deep forecasting methods can achieve promising performance but generally lack interpretability, hindering trustworthiness and practical deployment in safety-critical applications such as auto-driving and healthcare. In this paper, we propose a novel interpretable model, iTFKAN, for credible time series forecasting. iTFKAN enables further exploration of model decision rationales and underlying data patterns due to its interpretability achieved through model symbolization. Besides, iTFKAN develops two strategies, prior knowledge injection, and time-frequency synergy learning, to effectively guide model learning under complex intertwined time series data. Extensive experimental results demonstrated that iTFKAN can achieve promising forecasting performance while simultaneously possessing high interpretive capabilities.

该研究解决了当前深度预测方法在可解释性方面的不足，这限制了在安全关键应用中的信任度和实际部署。本文提出了一种新颖的可解释模型iTFKAN，通过模型符号化实现可解释性，并结合先验知识注入和时频协同学习，从而有效引导复杂交织时间序列数据下的模型学习。实验结果表明，iTFKAN能够兼具优异的预测性能和高可解释能力。

iTFKAN：基于Kolmogorov-Arnold网络的可解释时间序列预测