提出可解释时间序列预测模型iTFKAN，兼顾精度与可信度。
iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network
- 基于柯尔莫哥洛夫-阿诺德网络实现模型符号化，提升可解释性。
- 引入先验知识注入与时频协同学习策略，增强复杂数据建模能力。
- 适合医疗、自动驾驶等需可信决策的安全关键场景。
随着时间推移，特定领域数据展现出可预测性，促使人们基于历史数据进行时间序列预测。然而，当前深度预测方法虽性能优异，普遍缺乏可解释性，限制了其在自动驾驶、医疗等安全关键应用中的信任度与实际部署。本文提出一种新型可解释模型iTFKAN，通过模型符号化实现决策逻辑与数据模式的可追溯性。此外，iTFKAN设计了先验知识注入与时频协同学习两种策略，有效引导复杂交织时间序列数据的学习过程。大量实验表明，iTFKAN在保持高预测性能的同时，具备出色的可解释能力。
原文摘要 · Abstract (English)
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.
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