Nonstationary stochastic modelling of multivariate long-term wind and wave data
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 valu...
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ftntunivathens:oai:dspace.lib.ntua.gr:123456789/34928 2023-05-15T14:20:54+02:00 Nonstationary stochastic modelling of multivariate long-term wind and wave data Stefanakos, CN Belibassakis, KA 2005 http://dspace.lib.ntua.gr/handle/123456789/34928 unknown info:eu-repo/semantics/openAccess free Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE Computer simulation Data acquisition Information analysis Offshore structures Random processes Time series analysis Wind effects Data model Stochastic model Time series Wave data Standardization info:eu-repo/semantics/conferenceObject 2005 ftntunivathens 2019-07-13T16:32:11Z 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. Conference Object Arctic National Technical University of Athens (NTUA): DSpace |
institution |
Open Polar |
collection |
National Technical University of Athens (NTUA): DSpace |
op_collection_id |
ftntunivathens |
language |
unknown |
topic |
Computer simulation Data acquisition Information analysis Offshore structures Random processes Time series analysis Wind effects Data model Stochastic model Time series Wave data Standardization |
spellingShingle |
Computer simulation Data acquisition Information analysis Offshore structures Random processes Time series analysis Wind effects Data model Stochastic model Time series Wave data Standardization Stefanakos, CN Belibassakis, KA Nonstationary stochastic modelling of multivariate long-term wind and wave data |
topic_facet |
Computer simulation Data acquisition Information analysis Offshore structures Random processes Time series analysis Wind effects Data model Stochastic model Time series Wave data Standardization |
description |
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. |
format |
Conference Object |
author |
Stefanakos, CN Belibassakis, KA |
author_facet |
Stefanakos, CN Belibassakis, KA |
author_sort |
Stefanakos, CN |
title |
Nonstationary stochastic modelling of multivariate long-term wind and wave data |
title_short |
Nonstationary stochastic modelling of multivariate long-term wind and wave data |
title_full |
Nonstationary stochastic modelling of multivariate long-term wind and wave data |
title_fullStr |
Nonstationary stochastic modelling of multivariate long-term wind and wave data |
title_full_unstemmed |
Nonstationary stochastic modelling of multivariate long-term wind and wave data |
title_sort |
nonstationary stochastic modelling of multivariate long-term wind and wave data |
publishDate |
2005 |
url |
http://dspace.lib.ntua.gr/handle/123456789/34928 |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE |
op_rights |
info:eu-repo/semantics/openAccess free |
_version_ |
1766293373109403648 |