元学习混合模型预测中国生猪价格方法研究

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中图分类号：F323.7 文献标志码：A 文章编号：2096-9902（2025）13-0001-05

Abstract：Accuratelypredictingpigpricefluctuationsisof greatsignificance tomaintaining thebalanceofmarket supply anddemand，optimizingproductiondecisionsandensuringthestableoperationoftheindustrialchain.Aimingattheshortcoming oftraditionalpredictionmethodsininsuffcientgeneralizationabilityinsmallsamplescenarios，thispaperconstructsahybrid predictionframeworkbasedonSeasonal-TrendDecompositionusingLOESS（STL）andModel-AgnosticMeta-Learning（MAML）. Specificall：First，STLdecompositionisusedtodecouplethetimeseriesdataofpigpricesintothreecomponents：trendterms， seasontermsandresidualterms，whichefectivelyenhances featureinterpretabilityandsuppresesnoiseinterference；thena dual-stagemeta-learningmechanismisdesigned-inthebasictrainingstage，LSTMandGRUnetworkarchitecturesareusedto conductmuti-taskpre-trainingonpigandporkpricestolearnsharedfeaturerepresentationsofpricefluctuationsacrossbreds; Intherapidadaptationstage，therapidmigrationofthemodelinsmallsampletargetscenariosisachievedthroughcolaborative optimizationofMAMLsinternalloopparameterfine-tuningandexterallopmeta-parameterupdate.Empiricalresultsshowthat thismethodhassignificantimprovementoverthebenchmarkmodelintermsofroot-mean-squareeror（RMSE）andmeanabsolute eror（MAE）indicators，providinganinterpretableandtransferabledecisionanalysistolfortheconstructionofagricultural economic early warning systems.

Keywords： pig price prediction; meta-learning; MAML; STL; LSTM和机器学习方法。（剩余8409字）