KFS: KAN based adaptive Frequency Selection learning architecture for long term time series forecasting

• URL: http://arxiv.org/abs/2508.00635v2
• Date: Wed, 06 Aug 2025 08:08:39 GMT
• Title: KFS: KAN based adaptive Frequency Selection learning architecture for long term time series forecasting
• Authors: Changning Wu, Gao Wu, Rongyao Cai, Yong Liu, Kexin Zhang,
• Abstract summary: We propose a KAN based adaptive Frequency Selection learning architecture (KFS) to address these challenges.<n>This framework tackles prediction challenges stemming from cross-scale noise interference and complex pattern modeling.<n>Experiments across multiple real-world time series datasets demonstrate that KT achieves state-of-the-art performance as a simple yet effective architecture.
• Score: 8.839783121363835
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multi-scale decomposition architectures have emerged as predominant methodologies in time series forecasting. However, real-world time series exhibit noise interference across different scales, while heterogeneous information distribution among frequency components at varying scales leads to suboptimal multi-scale representation. Inspired by Kolmogorov-Arnold Networks (KAN) and Parseval's theorem, we propose a KAN based adaptive Frequency Selection learning architecture (KFS) to address these challenges. This framework tackles prediction challenges stemming from cross-scale noise interference and complex pattern modeling through its FreK module, which performs energy-distribution-based dominant frequency selection in the spectral domain. Simultaneously, KAN enables sophisticated pattern representation while timestamp embedding alignment synchronizes temporal representations across scales. The feature mixing module then fuses scale-specific patterns with aligned temporal features. Extensive experiments across multiple real-world time series datasets demonstrate that KT achieves state-of-the-art performance as a simple yet effective architecture.

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