MULTIVARIATE SINGULAR SPECTRUM ANALYSIS: A GENERAL VIEW AND NEW VECTOR FORECASTING APPROACH
2013; World Scientific; Volume: 01; Issue: 01 Linguagem: Inglês
10.1142/s2335680413500051
ISSN2335-6812
AutoresHossein Hassani, Rahim Mahmoudvand,
Tópico(s)Diverse Interdisciplinary Research Innovations
ResumoInternational Journal of Energy and StatisticsVol. 01, No. 01, pp. 55-83 (2013) No AccessMULTIVARIATE SINGULAR SPECTRUM ANALYSIS: A GENERAL VIEW AND NEW VECTOR FORECASTING APPROACHHOSSEIN HASSANI and RAHIM MAHMOUDVANDHOSSEIN HASSANIExecutive Business Centre, The Business School, Bournemouth University, BH8 8EB, UK and RAHIM MAHMOUDVANDDepartment of Statistics, Shahid Beheshti University, Evin, Tehran, 1983963113, Iranhttps://doi.org/10.1142/S2335680413500051Cited by:98 Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail AbstractThe different forms of the multivariate singular spectrum analysis (SSA) and their associated forecasting algorithms are considered from both theoretical and practical points of view. The new multivariate vector forecasting algorithm is introduced and its uniqueness is evaluated. The performance of the new multivariate forecasting algorithm is assessed against the existent multivariate technique using various simulated and real data sets (namely European Electricity and Gas series). The forecasting results confirm that the performance of the new multivariate approach is more accurate than the current approach.The optimality of the window length and the number of eigenvalues in multivariate SSA are considered and various bounds are recommended. The effect of common components between two time series is evaluated through a simulation study. The concept of similarity and dissimilarity are also considered based on the matched components among series.Keywords:Multivariate singular spectrum analysisForecastingRecurrent and vector approachOptimalityEuropean Electricity and Gas series References N. Golyandina , V. Nekrutkin and A. Zhigljavsky , Analysis of Time Series Structure: SSA and related techniques ( Chapman & Hall , New York - London , 2001 ) . 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BREAKER11 October 2013 | International Journal of Energy and Statistics, Vol. 01, No. 03HYDROELECTRIC ENERGY FORECASTKEILA MARA CASSIANO, LUIZ ALBINO TEIXEIRA JÚNIOR, RAFAEL MORAIS DE SOUZA, MOISÉS LIMA DE MENEZES, JOSÉ FRANCISCO MOREIRA PESSANHA, and REINALDO CASTRO SOUZA11 October 2013 | International Journal of Energy and Statistics, Vol. 01, No. 03PROBABILISTIC WIND POWER FORECASTING USING A SINGLE FORECASTMEYSAM QADRDAN, MANSOUREH GHODSI, and JIANZHONG WU8 July 2013 | International Journal of Energy and Statistics, Vol. 01, No. 02RESIDENTIAL ELECTRICITY CONSUMPTION FORECASTING USING A GEOMETRIC COMBINATION APPROACHLUIZ ALBINO TEIXEIRA JÚNIOR, MOISÉS LIMA DE MENEZES, KEILA MARA CASSIANO, JOSÉ FRANCISCO MOREIRA PESSANHA, and REINALDO CASTRO SOUZA8 July 2013 | International Journal of Energy and Statistics, Vol. 01, No. 02ANALYSING AND FORECASTING EUROPEAN UNION ENERGY DATACHRISTINA BENEKI and EMMANUEL SIRIMAL SILVA8 July 2013 | International Journal of Energy and Statistics, Vol. 01, No. 02 Recommended Vol. 01, No. 01 Metrics History Received 14 January 2013 Revised 11 February 2013 Accepted 11 February 2013 Published: 27 March 2013 KeywordsMultivariate singular spectrum analysisForecastingRecurrent and vector approachOptimalityEuropean Electricity and Gas seriesPDF download
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