基于动态提示池的股票趋势预测终身学习算法

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中图分类号：TN919-34；TP391 文献标识码：A 文章编号：1004-373X（2025）10-0063-08

DOI：10.16652/j.issn.1004-373x.2025.10.011 引用格式：，，，等.基于动态提示池的股票趋势预测终身学习算法[J].现代电子技术，2025，48（10）：63-70.

Abstract：Stockdatabelongs tostreamingdatawithadistributionthatchangesovertime，making itextremelychallenging topredictstocktrends.Existingforecastingmethodsadapttothelatestdatadistributionbyretrainingmodelsonarollngbasis， neglectingrepetitivepaternsinhistoricaldata，resultingincatastrophicforgetingandadecreaseinmodelprediction performance.Toaddressaboveissue，aPoolTrainalgorithmisproposed.Inthisalgorithm，theknowledge learnedfromeach retrainingofthemodelisstoredinadynamichintpool，alowingittorememberoldknowledgewhilelearningnewtasks. Acordingtotheknowledgeinthedynamicselectioncombinationhintpol，thecommon hintscancompletediferentdata distribution tasks.TheexperimentalresultsontheCSI3OOdatasetshowthat，incomparisonwiththecurrentoptimalalgorithm DDG-DA，thePooTrainalgorithmcanimprovetheinformationcoefficent （IC），informationcoeffientratio（ICIR），rank informationcoefcient（RankIC），andrankinformationcoeffcientratio（RankICIR）by11.5%，11.41%，0.2%，and34.69%， respectively.Itshowsthattheproposedalgorithmcanrealizebeterresultsinpredictingstock trends，providingvaluable reference information for investors.

Keywords：stock trendprediction；dynamiccuepol;lifelong learning;rollingtraining;corelationcoefficent;information coefficient

0 引言

股票趋势反映着国家的宏观经济政策，影响着投资者的经济利益，预测股票趋势已成为机器学习领域的研究热点之一]。（剩余13418字）