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dc.contributor.authorAdedokun, Abdul-Baaki Dolapo
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.contributor.otherTartu Ülikool. Matemaatika ja statistika instituutet
dc.date.accessioned2022-06-15T08:20:50Z
dc.date.available2022-06-15T08:20:50Z
dc.date.issued2022
dc.identifier.urihttp://hdl.handle.net/10062/82596
dc.description.abstractTime series data are sometimes affected by multiple cycles of different lengths. There can be a weekly cycle (better sales on Fridays), a monthly pattern (better sales at the beginning of the month as people have more cash after payday), and the effects of calendar seasonality (more tourists during summer, so better sales) might be present also. How to model multiple seasonality in one model? In this thesis, one could compare, for example, TBATS models (which allow multiple seasonalities) to alternative approaches.en
dc.language.isoenget
dc.rightsopenAccesset
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjecteksponentsiaalne silumineet
dc.subjectexponential smoothingen
dc.subjectTBATSen
dc.subjectBATSen
dc.subjectsessoonsed mudelidet
dc.subjectcomplex seasonalitiesen
dc.subjectaegridade prognoosimineet
dc.subjecttime series forecastingen
dc.titleModeling complex seasonalitiesen
dc.typeinfo:eu-repo/semantics/masterThesiset


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