Nonstationary stochastic modelling of multivariate long-term wind and wave data

Stefanakos, CN; Belibassakis, KA

dc.contributor.author	Stefanakos, CN	en
dc.contributor.author	Belibassakis, KA	en
dc.date.accessioned	2014-03-01T02:50:10Z
dc.date.available	2014-03-01T02:50:10Z
dc.date.issued	2005	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34928
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-27744470000&partnerID=40&md5=93854386314a196c1d1e1928538ed4eb	en
dc.subject.other	Computer simulation	en
dc.subject.other	Data acquisition	en
dc.subject.other	Information analysis	en
dc.subject.other	Offshore structures	en
dc.subject.other	Random processes	en
dc.subject.other	Time series analysis	en
dc.subject.other	Wind effects	en
dc.subject.other	Data model	en
dc.subject.other	Stochastic model	en
dc.subject.other	Time series	en
dc.subject.other	Wave data	en
dc.subject.other	Standardization	en
dc.title	Nonstationary stochastic modelling of multivariate long-term wind and wave data	en
heal.type	conferenceItem	en
heal.identifier.secondary	OMAE2005-67461	en
heal.publicationDate	2005	en
heal.abstract	In the present work, a nonstationary stochastic model, which is suitable for the analysis and simulation of multivariate time series of wind and wave data, is being presented and validated. This model belongs to the class of periodically correlated stochastic processes with yearly periodic mean value and standard deviation (periodically correlated or cyclostationary stochastic process). First, the time series is appropriately transformed to become Gaussian using the Box-Cox transformation. Then, the series is decomposed, using an appropriate seasonal standardization procedure, to a periodic (deterministic) mean value and a (stochastic) residual time series multiplied by a periodic (deterministic) standard deviation. The periodic components are estimated using appropriate lime series of monthly data. The residual stochastic part, which is proved to be stationary, is modelled as a VARMA process. This way the initial process can be given the structure of a multivariate periodically correlated process. The present methodology permits a reliable reproduction of available information about wind and wave conditions, which is required for a number of applications. Copyright © 2005 by ASME.	en
heal.journalName	Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE	en
dc.identifier.volume	2	en
dc.identifier.spage	225	en
dc.identifier.epage	234	en