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Improving SSA Predictios by Inverse Distance Weighting

dc.contributor.authorAwichi, Richard O.
dc.contributor.authorMuller, Werner G.
dc.date.accessioned2026-03-13T05:56:49Z
dc.date.issued2013
dc.description.abstractThis paper proposes a method of utilizing spatial information to improve predictions in one dimensional time series analysis using singular spectrum analysis (SSA). It employs inverse distance weighting for spatial averaging and subsequently multivariate singular spectrum analysis (MSSA) for enhanced forecasts. The technique is exemplified on a data set for rainfall recordings from Upper Austria
dc.identifier.citationAwichi, R. O., & Müller, W. G. (2013). Improving SSA predictions by inverse distance weighting. REVSTAT-Statistical Journal, 11(1), 105–119.
dc.identifier.urihttps://ir.lirauni.ac.ug/handle/123456789/1092
dc.language.isoen
dc.publisherREVSTAT– Statistical Journal
dc.subjectsingular spectrum analysis
dc.subjectinverse distance weighting
dc.subjectspatio-temporal predictions.
dc.titleImproving SSA Predictios by Inverse Distance Weighting
dc.typeArticle

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