Bibliographic Details
| 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 |
