基于机器学习的物流需求预测模型研究

打开文本图片集

文章编号：1002-3100（2026）04-0001-05

Research on the Logistics Demand Forecasting Model Based on Machine Learning

关键词：物流需求预测；长短期记忆网络；XGBoost；混合特征空间；突变点预测中图分类号：F326.6；TP18 文献标志码：A DOI： 10.13714/j.cnki.1002-3100.2026.04.001

Abstract： In scenarios withsudden demand surgessuch as "Double11"and "618"，traditional forecasting methods struggleto balance dynamic timeseriespatersandstaticinfluencingfactors，leadingtoisueslikefitinglagsandlargepeak deviations.Thispaper proposesahybridforecastingmodelthatintegrateslongshrt-term memory（LSTM）and XGBoost.Thecorestrategyadopts te"7-day sliding window LSTM for temporal feature extraction + XGBoost for static factor modeling" approach. Specifically， XGBoost is used to modelstaticctorssuchaspromotnlabelsandlas，xtactnolnarorelaosndneatestaticeatrevetorseo types of vectorsareconcatenatedtoconstructahybridfeature space，enablingthecollaboratie modelingofdynamicandstaticfactor. Experimental results show that the prediction error of this model in scenarios with sudden demand surges is 15.7% lower thanthat of the traditionalARIMAmethod，provdingtchicalsupportfolgisticsenterprises toespondtodemandfluctatiosandoptiiesupply chain configurations.

Key words：logisticsdemandfrecasting;longshort-term memory （LSTM）; XGBoost; hybridfeaturespace;changepointforecasting

0引言

随着全球电商交易额攀升，物流行业面临规模扩张相关问题与运营挑战。（剩余6357字）