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基于CNN和GRU的混合股指预测模型研究.docx

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基于CNN和GRU的混合股指预测模型研究 Abstract Withtheincreasingimportanceofstockindexforecastinginthefinancialindustry,itiscrucialtodevelopaccurateandefficientpredictionmodels.Inthispaper,weproposeahybridstockindexpredictionmodelbasedonConvolutionalNeuralNetwork(CNN)andGatedRecurrentUnit(GRU).TheproposedmodelusesCNNtoextractfeaturesandGRUtomodelthetemporaldependenciesinthedata.Toevaluatetheperformanceofthemodel,weconductexperimentsontheS&P500indexandcompareourresultswiththoseobtainedfromotherpopularmodels.Theexperimentalresultsdemonstratethatourproposedmodeloutperformsothermodelsintermsofaccuracy,robustness,andcomputationalefficiency. Introduction Stockindexpredictionhasbecomeachallengingandimportanttaskinthefinancialindustryduetoitspotentialimpactsonthedecision-makingprocessofinvestors.Accuratepredictionofstockindexcanhelpinvestorstomakeinformeddecisionsaboutthebuyingandsellingofstocks.Therefore,variouspredictionmodelshavebeenproposedinrecentyears. Recently,deeplearningapproaches,suchasConvolutionalNeuralNetwork(CNN)andGatedRecurrentUnit(GRU),havebeensuccessfullyappliedtovariouspredictionproblems.CNNhasbeenwidelyusedforimagerecognitiontasksduetoitsabilitytocapturelocalandglobalfeatures.Furthermore,GRUhasbeenshowntobeeffectiveinmodelingthetemporaldependenciesinsequentialdata. Inthispaper,weproposeahybridstockindexpredictionmodelbasedonCNNandGRU.TheproposedmodelusesCNNtoextractfeaturesfromtheinputdataandGRUtocapturethetemporaldependenciesinherentinstockindexdata.Theresultingmodelprovidesapowerfultoolforaccurateandefficientstockindexprediction. RelatedWork Awiderangeofapproacheshasbeenproposedforstockindexprediction.Traditionalmethods,suchasAutoregressiveIntegratedMovingAverage(ARIMA)andExponentialSmoothing(ES),havebeenwidelyusedfortimeseriesforecasting.However,thesemethodsrequireaseriesofassumptionsthatmaynotbevalidinreal-worldscenarios,thuslimitingtheiraccuracy. Recently,severalmachinelearningmodels,includingdecisiontrees,supportvectormachines,andartificialneuralnetworks,havebeenap