Kolmogorov-Arnold Networks (KANs) for Time Series Analysis
<|reference_start|>Kolmogorov-Arnold Networks (KANs) for Time Series Analysis: 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.<|reference_end|>
@article{vaca-rubio2024kolmogorov-arnold,
    title={Kolmogorov-Arnold Networks (KANs) for Time Series Analysis},
    author={Cristian J. Vaca-Rubio, Luis Blanco, Roberto Pereira, and M`arius
  Caus},
    journal={arXiv preprint arXiv:2405.08790},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08790},
    primaryClass={eess.SP cs.AI cs.LG}
}