Kolmogorov-Arnold Networks (KAN) is a groundbreaking model recently proposed by the MIT team, representing a revolutionary approach with the potential to be a game-changer in the field. This innovative concept has rapidly garnered worldwide interest within the AI community. Inspired by the Kolmogorov-Arnold representation theorem, KAN utilizes spline-parametrized univariate functions in place of traditional linear weights, enabling them to dynamically learn activation patterns and significantly enhancing interpretability. In this paper, we explore the application of KAN to time series forecasting and propose two variants: T-KAN and MT-KAN. T-KAN is designed to detect concept drift within time series and can explain the nonlinear relationships between predictions and previous time steps through symbolic regression, making it highly interpretable in dynamically changing environments. MT-KAN, on the other hand, improves predictive performance by effectively uncovering and leveraging the complex relationships among variables in multivariate time series. Experiments validate the effectiveness of these approaches, demonstrating that T-KAN and MT-KAN significantly outperform traditional methods in time series forecasting tasks, not only enhancing predictive accuracy but also improving model interpretability. This research opens new avenues for adaptive forecasting models, highlighting the potential of KAN as a powerful and interpretable tool in predictive analytics.

翻译：Kolmogorov-Arnold网络（KAN）是麻省理工学院团队近期提出的一种突破性模型，代表了一种具有革命性的方法，有望成为该领域的变革者。这一创新概念迅速在人工智能界引起了全球范围的关注。受Kolmogorov-Arnold表示定理的启发，KAN采用样条参数化的单变量函数替代传统的线性权重，使其能够动态学习激活模式，并显著增强了可解释性。在本文中，我们探索了KAN在时间序列预测中的应用，并提出了两种变体：T-KAN与MT-KAN。T-KAN旨在检测时间序列内的概念漂移，并能通过符号回归解释预测结果与先前时间步之间的非线性关系，使其在动态变化的环境中具有高度可解释性。另一方面，MT-KAN通过有效揭示并利用多元时间序列中变量间的复杂关系，提升了预测性能。实验验证了这些方法的有效性，表明T-KAN与MT-KAN在时间序列预测任务中显著优于传统方法，不仅提高了预测准确性，也增强了模型的可解释性。本研究为自适应预测模型开辟了新途径，凸显了KAN作为预测分析中强大且可解释工具的潜力。