Time Series Analysis with Signature-Weighted Kolmogorov-Arnold Networks

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.