TFKAN: Time-Frequency KAN for Long-Term Time Series Forecasting

TL;DR

TFKAN is a novel dual-branch model that combines time and frequency domain analysis using KANs to improve long-term time series forecasting, capturing recurring patterns more effectively.

Contribution

This paper introduces TFKAN, the first to integrate frequency domain processing with KANs for enhanced long-term forecasting accuracy.

Findings

TFKAN outperforms state-of-the-art methods on multiple datasets.

Frequency domain features significantly improve forecasting accuracy.

The dual-branch architecture effectively captures both local and global dependencies.

Abstract

Kolmogorov-Arnold Networks (KANs) are highly effective in long-term time series forecasting due to their ability to efficiently represent nonlinear relationships and exhibit local plasticity. However, prior research on KANs has predominantly focused on the time domain, neglecting the potential of the frequency domain. The frequency domain of time series data reveals recurring patterns and periodic behaviors, which complement the temporal information captured in the time domain. To address this gap, we explore the application of KANs in the frequency domain for long-term time series forecasting. By leveraging KANs' adaptive activation functions and their comprehensive representation of signals in the frequency domain, we can more effectively learn global dependencies and periodic patterns. To integrate information from both time and frequency domains, we propose the $\textbf{T}$ime-$\textbf{F}$requency KAN (TFKAN). TFKAN employs a dual-branch architecture that independently processes features from each domain, ensuring that the distinct characteristics of each domain are fully utilized without interference. Additionally, to account for the heterogeneity between domains, we introduce a dimension-adjustment strategy that selectively upscales only in the frequency domain, enhancing efficiency while capturing richer frequency information. Experimental results demonstrate that TFKAN consistently outperforms state-of-the-art (SOTA) methods across multiple datasets. The code is available at https://github.com/LcWave/TFKAN.