Time Series Analysis with Signature-Weighted Kolmogorov-Arnold Networks

Rémi Genet
Additional contact information
Rémi Genet: DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique

Post-Print from HAL

Abstract: This presentation introduces the Signature-Weighted Kolmogorov-Arnold Network (SigKAN), a novel approach to time series analysis that combines KANs with path signatures. We will discuss the theoretical framework of SigKAN, focusing on its use of path signatures to capture temporal information without using standard recurrent architecture. The talk will detail how SigKAN employs a weighting strategy, utilizing KANs within a gated residual network applied to path signatures. The presentation will include a discussion of the method's efficacy compared to current state-of-the-art techniques in time series analysis, supported by empirical results on market volume prediction as well as volatility forecasting.

Date: 2024-11
References: Add references at CitEc
Citations:

Published in Turing Institute: Bridging Rough Paths and Deep Learning: New Frontiers, Nov 2024, London, United Kingdom

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04924141

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().