SigMA: Path Signatures and Multi-head Attention for Learning Parameters in fBm-driven SDEs

ArXi:2512.15088v2 Announce Type: replace Stochastic differential equations (SDEs) driven by fractional Brownian motion (fBm) are increasingly used to model systems with rough dynamics and long-range dependence, such as those arising in quantitative finance and reliability engineering. However, these processes are non-Markovian and lack a semimartingale structure, rendering many classical parameter estimation techniques inapplicable or computationally intractable beyond very specific cases.