Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretability

• URL: http://arxiv.org/abs/2406.02496v1
• Date: Tue, 4 Jun 2024 17:14:31 GMT
• Title: Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretability
• Authors: Kunpeng Xu, Lifei Chen, Shengrui Wang,
• Abstract summary: Kolmogorov-Arnold Networks (KAN) is a groundbreaking model recently proposed by the MIT team. KAN is designed to detect concept drift within time series and can explain the nonlinear relationships between predictions and previous time steps. T-KAN is designed to detect concept drift within time series and can explain the nonlinear relationships between predictions and previous time steps. MT-KAN, on the other hand, improves predictive performance by effectively uncovering and leveraging the complex relationships among variables.
• Score: 6.4314326272535896
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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