Missing-value completion of nonstationary time series of wave data

Athanassoulis, GA; Stefanakos, ChN

dc.contributor.author	Athanassoulis, GA	en
dc.contributor.author	Stefanakos, ChN	en
dc.date.accessioned	2014-03-01T02:48:41Z
dc.date.available	2014-03-01T02:48:41Z
dc.date.issued	1998	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34010
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0031646153&partnerID=40&md5=ad3dcb8b6b8f702e4068d3fc8acca126	en
dc.subject.other	Computer simulation	en
dc.subject.other	Fourier transforms	en
dc.subject.other	Mathematical models	en
dc.subject.other	Probability	en
dc.subject.other	Random processes	en
dc.subject.other	Time series analysis	en
dc.subject.other	Autocovariance function	en
dc.subject.other	Nonstationary time series	en
dc.subject.other	Offshore structures	en
dc.title	Missing-value completion of nonstationary time series of wave data	en
heal.type	conferenceItem	en
heal.publicationDate	1998	en
heal.abstract	A new methodology for the missing-value completion of an incomplete nonstationary time series of a certain structure is presented and applied to measured data. The method is based on the modelling of long-term time series of wave data as a nonstationary stochastic process with yearly-long periodic mean value and standard deviation (periodically correlated stochastic process), introduced by the authors (Athanassoulis and Stefanakos, 1995). After a detrending and seasonal standardization, a low-order ARMA model is fitted to the (incomplete) residual stationary series using appropriate estimation techniques. The raw spectrum, calculated as the Fourier transform of a consistent estimate of the corresponding autocovariance function, is used for the estimation of the ARMA coefficients and the variance of the residuals. The incomplete time series of uncorrelated residuals is then completed by means of simulated data with the same first-order probability structure, and used, along with the ARMA model and the estimated deterministic components, to construct a new time series of the same structure without missing values. The above procedure is applied to two measured time series with different percentage of missing values. Comparisons of various statistical characteristics of the initial (incomplete) and reconstructed (completed) time series are satisfactory.	en
heal.publisher	ASME, Fairfield, NJ, United States	en
heal.journalName	Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE	en