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arXiv · 2405.08790

Kolmogorov-Arnold Networks (KANs) for Time Series Analysis

Cristian J. Vaca-Rubio·Luis Blanco·Roberto Pereira·Màrius Caus
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Abstract

This paper introduces a novel application of Kolmogorov-Arnold Networks (KANs) to time series forecasting, leveraging their adaptive activation functions for enhanced predictive modeling. Inspired by the Kolmogorov-Arnold representation theorem, KANs replace traditional linear weights with spline-parametrized univariate functions, allowing them to learn activation patterns dynamically. We demonstrate that KANs outperforms conventional Multi-Layer Perceptrons (MLPs) in a real-world satellite traffic forecasting task, providing more accurate results with considerably fewer number of learnable parameters. We also provide an ablation study of KAN-specific parameters impact on performance. The proposed approach opens new avenues for adaptive forecasting models, emphasizing the potential of KANs as a powerful tool in predictive analytics.

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BibTeXRIS

Cristian J. Vaca-Rubio, Luis Blanco, Roberto Pereira, Màrius Caus. 2024-05-14. Kolmogorov-Arnold Networks (KANs) for Time Series Analysis. https://doi.org/10.1109/gcwkshp64532.2024.11100692

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