%0 Journal Article
%A Gorjão, Leonardo Rydin
%A Witthaut, Dirk
%A Lind, Pedro G.
%T jumpdiff : A Python library for statistical inference of jump-diffusion processes in observational or experimental data sets
%J Journal of statistical software
%V 105
%N 4
%@ 1548-7660
%C Los Angeles, Calif.
%I UCLA, Dept. of Statistics
%M FZJ-2023-01634
%P 1
%D 2023
%X We introduce a Python library, called jumpdiff, which includes all necessary functions to assess jump-diffusion processes. This library includes functions which compute a set of non-parametric estimators of all contributions composing a jump-diffusion process, namely the drift, the diffusion, and the stochastic jump strengths. Having a set of measurements from a jump-diffusion process, jumpdiff is able to retrieve the evolution equation producing data series statistically equivalent to the series of measurements. The back-end calculations are based on second-order corrections of the conditional moments expressed from the series of Kramers-Moyal coefficients. Additionally, the library is also able to test if stochastic jump contributions are present in the dynamics underlying a set of measurements. Finally, we introduce a simple iterative method for deriving secondorder corrections of any Kramers-Moyal coefficient.
%F PUB:(DE-HGF)16
%9 Journal Article
%U <Go to ISI:>//WOS:000923067600001
%R 10.18637/jss.v105.i04
%U https://juser.fz-juelich.de/record/1005789