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基于ARIMA模型对我国城镇单位就业人员工资总额的预测.docx

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基于ARIMA模型对我国城镇单位就业人员工资总额的预测 Title:ForecastingTotalWagesofUrbanUnitsinChinaUsingtheARIMAModel Abstract: ThetotalwagesofurbanunitsinChinaplayacrucialroleintheeconomicdevelopmentofthecountry.Accurateforecastingofthisvariablecanhelppolicymakers,businesses,andindividualsmakeinformeddecisionsanddevelopeffectivestrategies.Inthisstudy,theAutoRegressiveIntegratedMovingAverage(ARIMA)modelisusedtoforecastthetotalwagesofurbanunitsinChina.ByanalyzinghistoricaldataandemployingtheARIMAmodel,predictionscanbemadeforthefuturetrendofwagesinurbanareas.Thefindingsofthisresearchcanprovidevaluableinsightsforpolicymakersandstakeholders,supportingevidence-baseddecision-makinginthelabormarket. 1.Introduction Thetotalwagesofurbanunitsrepresentanessentialindicatorofeconomicgrowthandwellbeinginacountry.Theyreflecttheoverallincomelevelofaworkforceandprovidevaluableinformationforassessingthelivingstandardsofurbanresidents.Furthermore,totalwagesalsohaveimportantimplicationsforconsumptionpatterns,GDPgrowth,andsocialstability. Giventhesignificanceoftotalwages,accurateforecastingisessential.Theaccuracyoftheforecasteddatacaninfluencegovernmentpolicies,businessplanning,andpersonalfinancialdecisions.TheARIMAmodeliswidelyusedfortimeseriesforecastingduetoitsabilitytocapturebothtrendandseasonalityinthedata.ThisstudyaimstoutilizeARIMAmodelingtoforecastthetotalwagesofurbanunitsinChina. 2.Methodology 2.1DataCollection Toconductthisresearch,acomprehensivedatasetofhistoricaltotalwagesofurbanunitsinChinaisrequired.Thedatacanbeobtainedfromofficialstatisticalsources,suchastheNationalBureauofStatisticsofChina.Thedatasetshouldcoverasufficientperiod,allowingfortheanalysisoftrendsandseasonalitypatterns. 2.2ARIMAModel TheARIMAmodelconsistsofthreecomponents:Autoregression(AR),Integration(I),andMovingAverage(MA).TheARcomponentmodelsthelinearrelationshipbetweenanobservationandtheobservationsinprecedingperiods.TheIcomponentallowsforthedifferencingofthedatatomakeitstationary,removingtrendsandseasonality.TheMAcomponentmodels