基于卷积神经网络的汽车销量预测模型

doi:10.11896/jsjkx.200600104

摘要/Abstract

摘要： 传统使用网络搜索数据进行销量预测时多通过人工选取关键词,难以充分考虑所有关键词的搜索量信息。通过使用卷积神经网络提取数据特征,能够解决传统预测方法存在的关键词合成问题。文章首次将深度学习理念引入汽车销量预测领域,首先通过网络爬虫方式获取汽车相关的关键词与网络搜索量,然后根据网络搜索量数据和销量数据的特点设计一种基于卷积神经网络的汽车销量预测模型,并对2019年上半年大众汽车销量做出预测。实验结果显示,与RBF模型、ARIMA模型、ARIMA+RBF混合模型对比,卷积神经网络的预测精度更高,大众品牌的预测精度达到89.51%。由于春节以及新政策出台的影响,2月份为预测误差最大的月份;随着市场的回暖,3月份为预测精度最高的月份。该预测方法为销量预测领域的研究提供了一种新思路。

关键词: 百度指数, 大众汽车, 卷积神经网络, 销量预测

Abstract: Traditionally the kewords selection of web search data is a manual selection task.It is difficult to take all the keywords into consideration.Therefore,the deep learning method is introduced into the field of automobile sales prediction,and the deep learning model is used to extract features of web search data.At first,car-related keywords and online search volumes are collectedthrough web crawlers,and then a car sales prediction model of convolutional neural network is designed,based on the characteristics of web search data and sales data.The model is adopted to predict the sales of Volkswagen in the first half of 2019.The results show that the convolutional neural network can effectively predict car sales,and the accuracy of the prediction reaches 89.51%,compared with RBF,ARIMA and ARIMA+RBF model.Due to the impact of the Spring Festival and the implementation of new policies,it has the largest forecast error in February.However,it has the highest prediction accuracy in March as the market recovers.

Key words: Baidu index, Convolutional neural network, Sales forecast, Volkswagen

中图分类号:

P315.69

刘吉华, 张梦迪, 彭红霞, 贾兴平. 基于卷积神经网络的汽车销量预测模型[J]. 计算机科学, 2021, 48(6A): 178-183. https://doi.org/10.11896/jsjkx.200600104

LIU Ji-hua, ZHANG Meng-di, PENG Hong-xia, JIA Xing-ping. Automobile Sales Forecasting Model Based on Convolutional Neural Network[J]. Computer Science, 2021, 48(6A): 178-183. https://doi.org/10.11896/jsjkx.200600104

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