Arbitrary-order finite-time corrections for the Kramers-Moyal operator

TL;DR

This paper develops a comprehensive method to accurately reconstruct stochastic differential equations from empirical data by deriving finite-time corrections to the Kramers-Moyal operator, enabling better distinction between diffusion and jump processes.

Contribution

It introduces a full power-series expansion of the Kramers-Moyal generator, including arbitrary-order finite-time corrections, for improved stochastic process analysis from time-series data.

Findings

Derived finite-time correction terms for stochastic process estimation.

Demonstrated improved process distinction in jump-diffusion models.

Provided a practical implementation using Bell polynomials.

Abstract

With the aim of improving the reconstruction of stochastic evolution equations from empirical time-series data, we derive a full representation of the generator of the Kramers-Moyal operator via a power-series expansion of the exponential operator. This expansion is necessary for deriving the different terms in a stochastic differential equation. With the full representation of this operator, we are able to separate finite-time corrections of the power-series expansion of arbitrary order into terms with and without derivatives of the Kramers-Moyal coefficients. We arrive at a closed-form solution expressed through conditional moments, which can be extracted directly from time-series data with a finite sampling intervals. We provide all finite-time correction terms for parametric and non-parametric estimation of the Kramers-Moyal coefficients for discontinuous processes which can be easily implemented - employing Bell polynomials - in time-series analyses of stochastic processes. With exemplary cases of insufficiently sampled diffusion and jump-diffusion processes, we demonstrate the advantages of our arbitrary-order finite-time corrections and their impact in distinguishing diffusion and jump-diffusion processes strictly from time-series data.