Journal Article

FZJ-2023-01634

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png jumpdiff : A Python library for statistical inference of jump-diffusion processes in observational or experimental data sets

Gorjão, L. R. (Corresponding author) ; Witthaut, D.FZJ* ; Lind, P. G.

2023
UCLA, Dept. of Statistics Los Angeles, Calif.

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Please use a persistent id in citations: http://hdl.handle.net/2128/34257  doi:10.18637/jss.v105.i04

Abstract: 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.

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Contributing Institute(s):

1. Systemforschung und Technologische Entwicklung (IEK-STE)

Research Program(s):

1. 1112 - Societally Feasible Transformation Pathways (POF4-111) (POF4-111)
2. HGF-ZT-I-0029 - Helmholtz UQ: Uncertainty Quantification - from data to reliable knowledge (HGF-ZT-I-0029) (HGF-ZT-I-0029)

Appears in the scientific report 2023

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Database coverage:
; ; ; ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Essential Science Indicators ; IF >= 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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