Kolmogorov-Arnold Networks (KAN) for Time Series Classification and Robust Analysis
Date
2025
Authors
Dong, C.
Zheng, L.
Chen, W.
Editors
Sheng, Q.Z.
Jiang, J.
Zhang, W.E.
Wu, J.
Ma, C.
Dobbie, G.
Zhang, X.
Manolopoulos, Y.
Mansoor, W.
Jiang, J.
Zhang, W.E.
Wu, J.
Ma, C.
Dobbie, G.
Zhang, X.
Manolopoulos, Y.
Mansoor, W.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings, Part IV of the 20th International Conference Advanced Data Mining and Applications (ADMA 2024), as published in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2025 / Sheng, Q.Z., Jiang, J., Zhang, W.E., Wu, J., Ma, C., Dobbie, G., Zhang, X., Manolopoulos, Y., Mansoor, W. (ed./s), vol.15390 LNCS, pp.342-355
Statement of Responsibility
Chang Dong, Liangwei Zheng, and Weitong Chen
Conference Name
20th International Conference Advanced Data Mining and Applications (ADMA) (3 Dec 2024 - 5 Dec 2024 : Sydney, NSW, Australia)
Abstract
Kolmogorov-Arnold Networks (KAN) has recently attracted significant attention as a promising alternative to traditional Multi-Layer Perceptrons (MLP). Despite their theoretical appeal, KAN require validation on large-scale benchmark datasets. Time series data, which has become increasingly prevalent in recent years, especially univariate time series are naturally suited for validating KAN. Therefore, we conducted a fair comparison among KAN, MLP, and mixed structures. The results indicate that KAN can achieve performance comparable to, or even slightly better than, MLP across 128 time series datasets. We also performed an ablation study on KAN, revealing that the output is primarily determined by the base component instead of b-spline function. Furthermore, we assessed the robustness of these models and found that KAN and the hybrid structureMLP KAN exhibit significant robustness advantages, attributed to their lower Lipschitz constants. This suggests that KAN and KAN layers hold strong potential to be robust models or to improve the adversarial robustness of other models.
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Dissertation Note
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© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025