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SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time Series

Author

Listed:
  • Hugo Inzirillo
  • Rémi Genet

    (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

Abstract

—We propose a novel approach that enhances multivariate function approximation using learnable path signatures and Kolmogorov-Arnold networks (KANs). We enhance the learningcapabilities of these networks by weighting the values obtained by KANs using learnable path signatures, which capture important geometric features of paths. This combination allows for a more comprehensive and flexible representation of sequential and temporal data. We demonstrate through studies that our SigKANs with learnable path signatures perform better than conventional methods across a range of function approximation challenges. By leveraging path signatures in neural networks, this method offers intriguing opportunities to enhance performance in time series analysis and time series forecasting, among other fields.

Suggested Citation

  • Hugo Inzirillo & Rémi Genet, 2024. "SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time Series," Post-Print hal-04923998, HAL.
    • Handle: RePEc:hal:journl:hal-04923998
    as

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    Keywords

    Machine Learning;

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