IDEAS home Printed from https://ideas.repec.org/a/hin/complx/3662204.html
   My bibliography  Save this article

Big Data-Driven Macroeconomic Forecasting Model and Psychological Decision Behavior Analysis for Industry 4.0

Author

Listed:
  • Jie Liu
  • Muhammad Javaid

Abstract

With the advent of Industry 4.0, economic development has become a rapid information age. The content of macroeconomic forecast is very extensive, and the existence of big data technology can provide the government with multilevel, diversified, and complete information and comprehensively process, integrate, summarize, and classify these pieces of information. This paper forecasts the CPI value in the next 12 months according to the CPI in China in the recent 20 years. Compared with the traditional forecasting methods, the forecasting results have higher accuracy and timeliness. At the same time, the trend of growth rate of industrial value-added is analyzed, and the experiments on MAE and RMSE show that the method proposed in this paper has obvious advantages. It also analyzes the disadvantages of traditional psychological decision-making behavior analysis, introduces the development status and advantages of big data-driven psychological decision-making behavior analysis, and opens up new research ideas for psychological decision-making analysis.

Suggested Citation

  • Jie Liu & Muhammad Javaid, 2021. "Big Data-Driven Macroeconomic Forecasting Model and Psychological Decision Behavior Analysis for Industry 4.0," Complexity, Hindawi, vol. 2021, pages 1-11, November.
    • Handle: RePEc:hin:complx:3662204
    DOI: 10.1155/2021/3662204
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/complexity/2021/3662204.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/complexity/2021/3662204.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/3662204?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:3662204. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.