The Langevin Approach: An R Package for Modeling Markov Processes

Authors

  • Philip Rinn Institute of Physics and ForWind – Center for Wind Energy research, C.v.O. University Oldenburg https://orcid.org/0000-0002-9239-0864
  • Pedro G Lind Institute of Physics and ForWind – Center for Wind Energy research, C.v.O. University Oldenburg; Institute of Physics, University Osnabrück
  • Matthias Wächter Institute of Physics and ForWind – Center for Wind Energy research, C.v.O. University Oldenburg
  • Joachim Peinke Institute of Physics and ForWind – Center for Wind Energy research, C.v.O. University Oldenburg

DOI:

https://doi.org/10.5334/jors.123

Keywords:

R, stochastic processes, Markov processes, data analysis, time series

Abstract

We describe an R package developed by the research group Turbulence, Wind energy and Stochastics (TWiSt) at the Carl von Ossietzky University of Oldenburg, which extracts the (stochastic) evolution equation underlying a set of data or measurements. The method can be directly applied to data sets with one or two stochastic variables. Examples for the one-dimensional and two-dimensional cases are provided. This framework is valid under a small set of conditions which are explicitly presented and which imply simple preliminary test procedures to the data. For Markovian processes involving Gaussian white noise, a stochastic differential equation is derived straightforwardly from the time series and captures the full dynamical properties of the underlying process. Still, even in the case such conditions are not fulfilled, there are alternative versions of this method which we discuss briefly and provide the user with the necessary bibliography.

Downloads

Published

2016-08-23

Issue

Vol. 4 No. 1 (2016)

Section

Software Metapapers

License

Copyright (c) 2016 The Author(s)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:
  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).