用于时间序列的Kolmogorov-Arnold Networks：桥接预测能力与可解释性
Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretability
AI总结：
本文提出用于时间序列预测的KAN变体T-KAN和MT-KAN，通过样条参数化单变量函数替代线性权重，在提升预测准确性的同时显著增强模型可解释性并检测概念漂移。
摘要：
Kolmogorov-Arnold Networks (KAN) 是 MIT 团队最近提出的一个开创性模型，代表了一种具有改变该领域潜力的革命性方法。这一创新概念迅速引起了全球 AI 社区的关注。受 Kolmogorov-Arnold 表示定理启发，KAN 使用样条参数化的单变量函数代替传统线性权重，使其能够动态学习激活模式并显著增强可解释性。本文探索了 KAN 在时间序列预测中的应用，并提出两个变体：T-KAN 和 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 通过有效发现和利用多元时间序列中变量间的复杂关系来提升预测性能。实验验证了这些方法的有效性，表明 T-KAN 和 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，还改善了模型可解释性。本研究为自适应预测模型开辟了新途径，突显了 KAN 作为预测分析中强大且可解释工具的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
