  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.
