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1、EuropeanJournalofOperationalResearch160(2005)501–514www.elsevier.com/locate/dswComputing,ArtificialIntelligenceandInformationTechnologyNeuralnetworkforecastingforseasonalandtrendtimeseriesa,*bG.PeterZhang,MinQiaDepartmentofManagement,J.MackRobinsonCollegeofBusiness,GeorgiaStateUniversity,35Bro
2、adStreet,NW,Atlanta,GA30303,USAbDepartmentofEconomics,CollegeofBusinessAdministration,KentStateUniversity,Kent,OH44242,USAReceived19October2001;accepted8August2003Availableonline18November2003AbstractNeuralnetworkshavebeenwidelyusedasapromisingmethodfortimeseriesforecasting.However,limitedem-
3、piricalstudiesonseasonaltimeseriesforecastingwithneuralnetworksyieldmixedresults.Whilesomefindthatneuralnetworksareabletomodelseasonalitydirectlyandpriordeseasonalizationisnotnecessary,othersconcludejusttheopposite.Inthispaper,weinvestigatetheissueofhowtoeffectivelymodeltimeserieswithbothseason
4、alandtrendpatterns.Inparticular,westudytheeffectivenessofdatapreprocessing,includingdeseasonalizationanddetrending,onneuralnetworkmodelingandforecastingperformance.BothsimulationandrealdataareexaminedandresultsarecomparedtothoseobtainedfromtheBox–Jenkinsseasonalautoregressiveintegratedmovingav
5、eragemodels.Wefindthatneuralnetworksarenotabletocaptureseasonalortrendvariationseffectivelywiththeunpreprocessedrawdataandeitherdetrendingordeseasonalizationcandramaticallyreduceforecastingerrors.Moreover,acombineddetr-endinganddeseasonalizationisfoundtobethemosteffectivedatapreprocessingapproac
6、h.Ó2003ElsevierB.V.Allrightsreserved.Keywords:Neuralnetworks;Box–Jenkinsmethod;Seasonality;Timeseries;Forecasting1.IntroductioncompaniedwiththeseasonalvariationsandcanhaveasignificantimpactonvariousforecastingManybusinessandeconomictimeseriesexhibitmethods.Atimeserieswithtrendisconsideredtosea
7、sonalandtrendvariations.Seasonalityisabenonstationaryandoftenneedstobemadesta-periodicandrecurrentpatterncausedbyfactorstionarybeforemostmodelingandforecastingsuchasweather,holidays,repeatingpromotions,processestakeplace.Accurateforecastingof