Time series forecasting with Hahn Kolmogorov-Arnold networks
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| Format: | Preprint |
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2026
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| _version_ | 1866917330850676736 |
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| author | Hasan, Md Zahidul Hamza, A. Ben Bouguila, Nizar |
| author_facet | Hasan, Md Zahidul Hamza, A. Ben Bouguila, Nizar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18837 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Time series forecasting with Hahn Kolmogorov-Arnold networks Hasan, Md Zahidul Hamza, A. Ben Bouguila, Nizar Machine Learning 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. |
| title | Time series forecasting with Hahn Kolmogorov-Arnold networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.18837 |