摘要
物流业景气指数是反映经济发展的先导性指标,准确预测物流业景气指数对于辅助政府部门科学制定经济调控政策,指导企业开展经营活动具有重要意义。提出PSO-SVM的组合预测模型,动态调整单一预测模型的训练集和测试集,计算相邻两个单一模型平均值作为总体模型测试(预测)值,并以福建省物流业景气指数预测作为实证研究,建模阶段的均方根相对误差为1.26%,测试阶段的均方根相对误差为0.82%。结果表明,PSO-SVM组合预测模型拟合及测试都达到很高的精度。
Logistics prosperity index is a leading indicator reflecting economic development.Accurate prediction of logistics prosperity index is of great significance for assisting government to scientifically formulate economic regulation policies and guiding enterprises to conduct operational activities.A combination forecast model based on PSO-SVM(particle swarm optimization-support vector machine)is proposed,and the training set and the test set of the single forecast model are adjusted dynamically.The average value of the two adjacent single models is calculated as test(prediction)value of the total model.Taking logistics prosperity index forecast of Fujian province as an empirical subject,the relative error of the root mean square in the modeling stage is 1.26%,and the relative error of the root mean square in the testing stage is 0.82%.The results show that the accuracy of fitting and testing of the combination forecast model based on PSO-SVM is high.
作者
陈东清
黄章树
叶翀
CHEN Dong-qing;HUANG Zhang-shu;YE Chong(Fuzhou University Zhicheng College, Fuzhou 350002, China;School of Economics and Management, Fuzhou University, Fuzhou 350108, China)
出处
《北京邮电大学学报（社会科学版）》
2020年第6期52-62,共11页
Journal of Beijing University of Posts and Telecommunications(Social Sciences Edition)
基金
国家社科基金项目(19FJYB043)
福建省中青年教师教育科研项目(社科类)(JAS180839)。
关键词
物流业景气指数
组合预测
支持向量机
粒子群算法
logistics prosperity index
combination forecast
support vector machine
particle swarm optimization
