基于网络搜索数据和深度神经网络的社会消费品零售总额预测研究  

Predicting Total Retail Sales of Consumer Goods Based on Web Search Data and Deep Neural Networks

作　　者：程开明[1] 刘书成 雷洛 陈晓颖 CHENG Kaiming;LIU Shucheng;LEI Luo;CHEN Xiaoying(School of Statistics and Mathematics,Zhejiang Gongshang University,Hangzhou 310018,China;School of Economics,Xiamen University,Xiamen 361005,China)

机构地区：[1]浙江工商大学统计与数学学院,浙江杭州310018 [2]厦门大学经济学院,福建厦门361005

出　　处：《运筹与管理》2024年第12期203-209,I0091-I0096,共13页Operations Research and Management Science

基　　金：国家社会科学基金重大招标项目(20&ZD133)。

摘　　要：为弥补传统预测变量及预测技术的不足,本文基于深度学习长期和短期时间序列网络(LSTNet),结合网络搜索数据与政府统计指标,构建LSTNet&BI模型开展浙江省及地级市社会消费品零售总额的预测研究。研究发现:(1)引入网络搜索数据能够有效提高LSTNet模型的预测性能与预测精度;(2)LSTNet&BI模型具有较好的泛化能力,对浙江省社会消费品零售总额的短期和长期预测效果较稳定,其预测性能与预测精度均优于其他基准模型;(3)LSTNet&BI模型具备较强的稳健性,对杭州市、绍兴市和衢州市社会消费品零售总额的预测效果也较好。Total retail sales of consumer goods is a key indicator of social consumption demand,and can provide a relatively comprehensive picture of consumers’comprehensive judgement of the current and future economic situation,market fluctuations and personal income expectations.At present,the forecast of total retail sales of consumer goods is mostly based on traditional government statistics,which is not ideal due to the single system of forecasting indicators and the lack of micro information reflecting the consumption of social groups.In the context of big data,the introduction of web search data can effectively break through the limitations of traditional data such as small capacity and poor timeliness,and help improve the performance of the forecasting model.

关 键 词：社会消费品零售总额 网络搜索数据 深度神经网络 LSTNet&BI模型 

分 类 号：F713.32[经济管理—产业经济] TP183[自动化与计算机技术—控制理论与控制工程]