skip to main content
10.1145/3414752.3414759acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicemeConference Proceedingsconference-collections
research-article

Automobile Sales Forecast Based on Web Search and Social Network Data

Published:18 November 2020Publication History

ABSTRACT

The information on the Internet will affect consumers’ purchase decisions, especially for high-value products such as automobiles, so some data from search engine and We Media platform to a certain extent will reflect the car sales trend. This paper applies the monthly sales data of Audi from 2011 to 2019 to explore the impact of Baidu and Weibo data on automobile sales forecast. By adding car sales data, Baidu search indexes of selected keywords, Weibo text amount indexes and Weibo sentiment indexes, three machine learning models are established to compare with traditional time series model, thus key indexes helping improve the prediction accuracy are analyzed and marketing advice will be given to car companies.

References

  1. Xie Tianbao,Cui Tian. Research on Automobile Sales Forecast Based on Internet Search Data [J].Information Technology and Network Security,2018,37(08):50-53.Google ScholarGoogle Scholar
  2. http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/ssbg/201910/P020191025506904765613.pdfGoogle ScholarGoogle Scholar
  3. Dou Zixin. Sales Performance Predicting by Using Online Reviews of Textual Polarity Sentiment [D].Guangzhou University,2019.Google ScholarGoogle Scholar
  4. https://tech.sina.com.cn/i/2020-02-26/doc-iimxxstf4598954.shtml?cref=cjGoogle ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Other conferences
    ICEME '20: Proceedings of the 2020 11th International Conference on E-business, Management and Economics
    July 2020
    312 pages
    ISBN:9781450388016
    DOI:10.1145/3414752

    Copyright © 2020 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 18 November 2020

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format .

View HTML Format