基于两阶段组合预测模型的区域物流需求预测  

Regional Logistics Demand Forecasting Based on Two-stage Combination Prediction Model

作　　者：李捷 陈彦如[1] 杨璐[1] 

机构地区：[1]西南交通大学经济管理学院,四川成都610031

出　　处：《信息与控制》2018年第2期247-256,共10页Information and Control

基　　金：国家自然科学基金资助项目(71402149)

摘　　要：基于支持向量机、遗传算法、粒子群算法和BP神经网络,提出了两阶段组合预测模型GSPS-BPNN,旨在将预测问题分为2个阶段.第1阶段预测为第2阶段预测提供更有效的样本数据特征,并降低数据维度,提高算法收敛性;第2阶段预测可以弱化第1阶段预测模型可能产生的过学习的影响.最后将GSPS-BPNN模型用于成都市和天津市物流需求预测中,结果显示GSPS-BPNN模型在预测精度及预测稳定性方面优于单阶段单一预测模型.We propose a two-stage combination prediction model called GSPS-back propagation neural network( BPNN) based on support vector machine,genetic algorithm,particle swarm optimization and BPNN. The first stage of the model provides more features to handle data effectively for the second stage,reduce the data dimensions,and improve the convergence of the algorithm. The second stage of the model addresses the overfitting issue that may occur in the first stage. Two numerical examples are presented. Results show that the GSPS-BPNN model has better accuracy and stability than a one-stage prediction model.

关 键 词：支持向量机 遗传算法 粒子群优化 神经网络 两阶段组合预测 

分 类 号：F252[经济管理—国民经济] TP18[自动化与计算机技术—控制理论与控制工程]