Adedokun, Abdul-Baaki DolapoTartu Ülikool. Loodus- ja täppisteaduste valdkondTartu Ülikool. Matemaatika ja statistika instituut2022-06-152022-06-152022http://hdl.handle.net/10062/82596Time 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.engopenAccessAttribution-NonCommercial-NoDerivatives 4.0 Internationaleksponentsiaalne silumineexponential smoothingTBATSBATSsessoonsed mudelidcomplex seasonalitiesaegridade prognoosiminetime series forecastinginfo:eu-repo/semantics/masterThesis