Kolmogorov-Arnold Networks (KANs) for Time Series Analysis
| Title: | Kolmogorov-Arnold Networks (KANs) for Time Series Analysis |
|---|---|
| Authors: | Vaca-Rubio, Cristian J., Blanco, Luis, Pereira, Roberto, Caus, Màrius |
| Publication Year: | 2024 |
| Collection: | Computer Science |
| Subject Terms: | Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| More Details: | 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. |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/2405.08790 |
| Accession Number: | edsarx.2405.08790 |
| Database: | arXiv |
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