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Algorithms of the Copula Fit to the Nonlinear Processes in the Utility Industry
Riga Technical University, Latvia.ORCID iD: 0000-0001-9438-3441
Riga Technical University, Latvia.ORCID iD: 0000-0001-8001-7037
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (MAM)ORCID iD: 0000-0002-0139-0747
2017 (English)In: Procedia Computer Science, E-ISSN 1877-0509, Vol. 104, p. 572-577Article in journal (Refereed) Published
Abstract [en]

Our research studies the construction and estimation of copula-based semi parametric Markov model for the processes, which involved in water flows in the hydro plants. As a rule analyzing the dependence structure of stationary time series regressive models defined by invariant marginal distributions and copula functions that capture the temporal dependence of the processes is considered. This permits to separate out the temporal dependence (such as tail dependence) from the marginal behavior (such as fat tails) of a time series. Dealing with utility company data we have found the best copula describing data - Gumbel copula. As a result constructed algorithm was used for an imitation of low probability events (in a hydro power industry) and predictions.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 104, p. 572-577
Keywords [en]
Copula; Diffusion processes; Time series; Semi parametric regressions
National Category
Probability Theory and Statistics
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-34990DOI: 10.1016/j.procs.2017.01.174ISI: 000399478800076Scopus ID: 2-s2.0-85016014425OAI: oai:DiVA.org:mdh-34990DiVA, id: diva2:1078168
Conference
ICTE 2016, December 2016, Riga, Latvia
Available from: 2017-03-02 Created: 2017-03-02 Last updated: 2022-02-10Bibliographically approved

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Malyarenko, Anatoliy

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