Electrical Engineering and Systems Science > Signal Processing
[Submitted on 14 May 2024]
Title:Kolmogorov-Arnold Networks (KANs) for Time Series Analysis
View PDF HTML (experimental)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.
Submission history
From: Cristian Jesús Vaca Rubio [view email][v1] Tue, 14 May 2024 17:38:17 UTC (281 KB)
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