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Comparison of Causal and Time-Series Models in Forecasting: Applications, Challenges, and Techniques

This paper compares causal and time-series models in forecasting, analyzing their applications, challenges, and techniques.

Mia Martinez
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ComparisonofCausalandTime-SeriesModelsinForecasting:Applications,Challenges,andTechniquesDiscussion2Whatisthedifferencebetweenacausalmodelandatime-seriesmodel?Giveanexampleofwheneachwouldbeused.Whataresomeoftheproblemsanddrawbacksofthemovingaverageforecastingmodel?Howdoyoudeterminehowmanyobservationstoaverageinamovingaveragemodel?Howdoyoudeterminetheweightingstouseinaweightedmovingaveragemodel?Whatisthedifferencebetweenacausalmodelandatime-seriesmodel?Giveanexampleofwheneachwouldbeused.Thecasualmodelisbroaderinthenumberoffactorsthatareanalyzedwhendeterminingtheoutcomeoftheforecastingexercise.Thismethodisusefulwhenyouneedtoapplyseveraldifferentcomponentstotheanalysisinordertoreceiveawell-roundedforecast.Thetime-seriesmodelusestimetodeterminetheoutcomeoftheforecastingrequirement.Thetime-seriesmethoduseshistoricaldataoverasetperiodtodeterminetheresultoftheforecasting.Thisisusefulinamorelimitedscenariothatisbasicinnature.Thecasualmodelwouldbeusefulindeterminingtheforecastofabonussysteminwhichseveraldifferentfactorsareappliedtothefinalvaluebeingdistributed.Thetime-seriesmodelwouldbeusefulindeterminingtrafficlevelsinaretailstorebasedsolelyontimeofdayandnoanyotherfactorssuchasseasonalelementsorotherdata.FYIOPJPYSUISIfPAUUUPIUTOYSOYSUNSAPPSUPTSUNSUPESUSUI|veT=StudyXY

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Business Management

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