Time series forecasting with Hahn Kolmogorov-Arnold networks

• URL: http://arxiv.org/abs/2601.18837v1
• Date: Sun, 25 Jan 2026 21:28:24 GMT
• Title: Time series forecasting with Hahn Kolmogorov-Arnold networks
• Authors: Md Zahidul Hasan, A. Ben Hamza, Nizar Bouguila,
• Abstract summary: HaKAN is a versatile model based on Kolmogorov-Arnold Networks (KANs)<n>Our model integrates learnable activation functions and providing a lightweight and interpretable alternative for time series forecasting.<n>Our model consistently outperforms recent state-of-the-art methods, with ablation studies validating the effectiveness of its core components.
• Score: 20.88954806378471
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent Transformer- and MLP-based models have demonstrated strong performance in long-term time series forecasting, yet Transformers remain limited by their quadratic complexity and permutation-equivariant attention, while MLPs exhibit spectral bias. We propose HaKAN, a versatile model based on Kolmogorov-Arnold Networks (KANs), leveraging Hahn polynomial-based learnable activation functions and providing a lightweight and interpretable alternative for multivariate time series forecasting. Our model integrates channel independence, patching, a stack of Hahn-KAN blocks with residual connections, and a bottleneck structure comprised of two fully connected layers. The Hahn-KAN block consists of inter- and intra-patch KAN layers to effectively capture both global and local temporal patterns. Extensive experiments on various forecasting benchmarks demonstrate that our model consistently outperforms recent state-of-the-art methods, with ablation studies validating the effectiveness of its core components.

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