基于ARIMA-LSTM的物流網(wǎng)絡(luò)分揀中心貨量預(yù)測模型探究

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中圖分類號：0211 文獻標(biāo)志碼：A 文章編號：2095-2945（2025）13-0046-05

Abstract：Withthevigorousdevelopmentofe-commercelogisticsnetworks，enhancinglogisticstransportationficiencyand reducinglaborcostshavebecomecoreelementsforthelogisticsindustrytostrengthenitscompetitivenessThisstudyfocuseson thecargovolumeforecastingfortransportroutesinthesortingcentersofe-commercelogisticsnetworks，aiming toacurately depictthedailyandevenhourlyfluctuationsincargovolumeoverthenext3Odaysthroughin-depthminingofhistoricalcargo volumedata.Specificall，wefirstconducted meticulouspreprocessingofdailycargovolumedatafromthepastfourmonthsand hourlycargovolumedatafromthepast3Odaysfrom57sortingcenters.Onthisbasis，weconstructedanAutoregresive IntegratedMovingAverage（ARIMA）modeltopredictthedailycargovolumeprofileoverthenext3Odays.Furthermore，we introducedaLong Short-TermMemory（LSTM）neuralnetworkmodeltoachieveprecisepredictionsof hourlycargovolumedata for the next 30 days.

Keywords： logistics network; transportation route;cargo volume profile; ARIMA model; LSTM model

現(xiàn)代物流是國民經(jīng)濟的核心，對經(jīng)濟發(fā)展至關(guān)重要，能促進市場繁榮、推動高質(zhì)量發(fā)展。（剩余4099字）