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ARIMA模型在居民消费价格指数预测中的应用研究.docx

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ARIMA模型在居民消费价格指数预测中的应用研究 Abstract TheapplicationofARIMAmodelinthepredictionofconsumerpriceindex(CPI)hasbecomeapopulartopicinrecentyears.TheARIMAmodelisatimeseriesforecastingmodelthatiswidelyusedtopredictfuturetrendsandpatternsindata.Inthispaper,weconductacomprehensivestudyontheapplicationoftheARIMAmodelinCPIprediction.ThestudymainlyfocusesonthetheoreticalfoundationoftheARIMAmodel,methodsofparameterselection,modelfitting,andanalysisofthepredictionresults.Furthermore,wealsocomparethepredictionresultsoftheARIMAmodelwithothermethodstoevaluateitseffectivenessandefficiency.ThestudyprovidesavaluableinsightintotheapplicationofARIMAinCPIprediction,whichcancontributetobettereconomicplanninganddecision-making. Introduction Theconsumerpriceindexisanimportanteconomicindicatorthatmeasurestheaveragepricelevelofgoodsandservicespurchasedbyhouseholds.ThepredictionofCPIiscrucialforgovernmentpolicymakers,investors,andconsumers,asitinfluenceseconomicpolicydecisions,investmentstrategies,andpersonalfinancialplanning.TheARIMAmodelisapowerfultoolforforecastingtimeseriesdata,andithasbeenwidelyusedinvariousfields,includingeconomics,finance,andengineering.Inrecentyears,theARIMAmodelhasalsobeenappliedtoforecastCPIdata.ThispaperaimstoinvestigatetheapplicationofARIMAinCPIpredictionandtoprovideacomprehensivestudyonthetopic. Theoreticalfoundation TheARIMAmodelisatimeseriesforecastingmodelthatdescribestherelationshipbetweenthecurrentobservationandthepastobservationsofthesametimeseries.Itrequiresthreecomponents:autoregression(AR),integration(I),andmovingaverage(MA).TheARcomponentrepresentsthelinearregressionofthecurrentobservationonthepastobservations.TheMAcomponentrepresentsthelinearregressionofthecurrentobservationonthepasterrorterms.TheIcomponentrepresentsthenumberoftimesthedataisdifferencedtoachievestationarity.TheARIMAmodelisdenotedasARIMA(p,d,q),wherepistheorderoftheARcomponent,distheorderofintegration,andqistheorderoftheMAcomponent. Methodsofparameterselection TheselectionofARIMAparametersisac