Journal Article

FZJ-2020-00576

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Analysis and data-driven reconstruction of bivariate jump-diffusion processes

Rydin Gorjão, L. (Corresponding author)FZJ* ; Heysel, J. ; Lehnertz, K. ; Tabar, M. R. R.

2019
APS Woodbury, NY

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Please use a persistent id in citations: http://hdl.handle.net/2128/24314  doi:10.1103/PhysRevE.100.062127

Abstract: We introduce the bivariate jump-diffusion process, consisting of two-dimensional diffusion and two-dimensional jumps, that can be coupled to one another. We present a data-driven, nonparametric estimation procedure of higher-order (up to 8) Kramers-Moyal coefficients that allows one to reconstruct relevant aspects of the underlying jump-diffusion processes and to recover the underlying parameters. The procedure is validated with numerically integrated data using synthetic bivariate time series from continuous and discontinuous processes. We further evaluate the possibility of estimating the parameters of the jump-diffusion model via data-driven analyses of the higher-order Kramers-Moyal coefficients, and the limitations arising from the scarcity of points in the data or disproportionate parameters in the system.

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Contributing Institute(s):

1. Systemforschung und Technologische Entwicklung (IEK-STE)

Research Program(s):

1. 153 - Assessment of Energy Systems – Addressing Issues of Energy Efficiency and Energy Security (POF3-153) (POF3-153)
2. ES2050 - Energie Sytem 2050 (ES2050) (ES2050)
3. VH-NG-1025 - Helmholtz Young Investigators Group "Efficiency, Emergence and Economics of future supply networks" (VH-NG-1025_20112014) (VH-NG-1025_20112014)

Appears in the scientific report 2019

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Web of Science Core Collection

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