Ilya Chevyrev

Characteristic functions of measures on geometric rough paths

We define a characteristic function for probability measures on the signatures of geometric rough paths. We determine sufficient conditions under which a random variable is uniquely determined by its expected signature, thus partially solving the analogue of the moment problem. We furthermore study analyticity properties of the characteristic function and prove a method of moments for weak convergence of random variables. We apply our results to signature arising from Levy, Gaussian and Markovian rough paths.

Random walks and Lévy processes as rough paths

We consider random walks and Lévy processes in a homogeneous group G. For all

Examples of Renormalized SDEs

p > 0

A Primer on the Signature Method in Machine Learning

p>0, we completely characterise (almost) all G-valued Lévy processes whose sample paths have finite p-variation, and give sufficient conditions under which a sequence of G-valued random walks converges in law to a Lévy process in p-variation topology. In the case that G is the free nilpotent Lie group over

An isomorphism between branched and geometric rough paths

\mathbb {R}^d

Unitary representations of geometric rough paths

Rd, so that processes of finite p-variation are identified with rough paths, we demonstrate applications of our results to weak convergence of stochastic flows and provide a Lévy–Khintchine formula for the characteristic function of the signature of a Lévy process. At the heart of our analysis is a criterion for tightness of p-variation for a collection of càdlàg strong Markov processes.