Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/39243

TitleForecasting time series combining Holt-Winters and bootstrap approaches
Author(s)Costa, Marco
Gonçalves, A. Manuela
Silva, Joana
KeywordsTime series
Exponential smoothing methods
Holt-Winters method
Bootstrap
Forecasting
Prediction intervals
Issue dateMar-2015
PublisherAIP Publishing
JournalAIP Conference Proceedings
Abstract(s)Exponential smoothing methods are the most used in time series modeling and forecasting, due to their versatility and the vast model option they integrate. Also, within the computing statistical area, Bootstrap methodology is widely applied in statistical inference concerning time series. Therefore, this study's main objective is to analyse Holt-Winters exponential smoothing method's performance associated to Bootstrap methodology, as an alternative procedure for modeling and forecasting in time series. The Bootstrap methodology combined with Holt-Winters methodology is applied to a study case on an environmental time series concerning a surface water quality variable, Dissolved Oxygen (DO). The proposed procedure allows to obtaining better point forecasts and interval forecasts with less amplitude than those obtained by means of the usual methods.
TypeConference paper
DescriptionPublicado em "AIP Conference Proceedings", Vol. 1648
URIhttp://hdl.handle.net/1822/39243
ISBN9780735412873
DOI10.1063/1.4912411
ISSN0094-243X
Publisher versionhttp://dx.doi.org/10.1063/1.4912411
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CMAT - Artigos em atas de conferências e capítulos de livros com arbitragem / Papers in proceedings of conferences and book chapters with peer review

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