Kolmogorov-Arnold Networks (KANs) for Time Series Analysis

• URL: http://arxiv.org/abs/2405.08790v2
• Date: Wed, 25 Sep 2024 12:47:46 GMT
• Title: Kolmogorov-Arnold Networks (KANs) for Time Series Analysis
• Authors: Cristian J. Vaca-Rubio, Luis Blanco, Roberto Pereira, Màrius Caus,
• Abstract summary: We introduce a novel application of Kolmogorov-Arnold Networks (KANs) to time series forecasting. Inspired by the Kolmogorov-Arnold representation theorem, KANs replace traditional linear weights with spline-parametrized univariate functions. We demonstrate that KANs outperforms conventional Multi-Layer Perceptrons (MLPs) in a real-world satellite traffic forecasting task.
• Score: 6.932243286441558
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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