KANMixer: Can KAN Serve as a New Modeling Core for Long-term Time Series Forecasting?

• URL: http://arxiv.org/abs/2508.01575v1
• Date: Sun, 03 Aug 2025 04:03:13 GMT
• Title: KANMixer: Can KAN Serve as a New Modeling Core for Long-term Time Series Forecasting?
• Authors: Lingyu Jiang, Yuping Wang, Yao Su, Shuo Xing, Wenjing Chen, Xin Zhang, Zhengzhong Tu, Ziming Zhang, Fangzhou Lin, Michael Zielewski, Kazunori D Yamada,
• Abstract summary: We introduce KANMixer, a concise architecture integrating a multi-scale mixing backbone that fully leverages KAN's adaptive capabilities.<n>We show that KANMixer achieves state-of-the-art performance in 16 out of 28 experiments across seven benchmark datasets.
• Score: 17.96421618979159
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, multilayer perceptrons (MLP)-based deep learning models have demonstrated remarkable success in long-term time series forecasting (LTSF). Existing approaches typically augment MLP backbones with hand-crafted external modules to address the inherent limitations of their flat architectures. Despite their success, these augmented methods neglect hierarchical locality and sequential inductive biases essential for time-series modeling, and recent studies indicate diminishing performance improvements. To overcome these limitations, we explore Kolmogorov-Arnold Networks (KAN), a recently proposed model featuring adaptive basis functions capable of granular, local modulation of nonlinearities. This raises a fundamental question: Can KAN serve as a new modeling core for LTSF? To answer this, we introduce KANMixer, a concise architecture integrating a multi-scale mixing backbone that fully leverages KAN's adaptive capabilities. Extensive evaluation demonstrates that KANMixer achieves state-of-the-art performance in 16 out of 28 experiments across seven benchmark datasets. To uncover the reasons behind this strong performance, we systematically analyze the strengths and limitations of KANMixer in comparison with traditional MLP architectures. Our findings reveal that the adaptive flexibility of KAN's learnable basis functions significantly transforms the influence of network structural prior on forecasting performance. Furthermore, we identify critical design factors affecting forecasting accuracy and offer practical insights for effectively utilizing KAN in LTSF. Together, these insights constitute the first empirically grounded guidelines for effectively leveraging KAN in LTSF. Code is available in the supplementary file.

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