Time Series Forecasting for Regional Development Composite Index Using Real-Time Floating Population Data

Composite development indices show an exponential movement of major economic indicators to identify and predict the overall trend of the national economy. However, the existing method of writing composite development indices is based on simple statistical methods using macroscopic data. Therefore, it presents limitations when grasping regional economic trends late. It is because the time of announcement of composite development indices is concentrated at the end of each month, quarter, and year. This study used the floating population estimated from smartphone data that can be collected in real-time to analyze how floating population patterns affect regional economic situations to compensate for these limitations. The primary purpose was to present a prompt development prediction methodology that reflects this meaningful relationship. A correlation and cross-correlation analysis was performed to exhibit a clear relationship between composite development indices and floating population value. In addition, a time series model and a multiple regression model analyses were applied to predict regional development indices. The results obtained facilitated the prompt selection of regional composite indices after choosing a model that exhibits high prediction accuracy and efficiency of the application. The selected regional development composite indices are expected to be used as a faster and more reliable prediction criterion than the existing development composite indices used to predict a specific city’s economic situation.

• Record URL:
  • https://doi.org/10.1155/2023/9586307
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/5121625
• Supplemental Notes:
  • © 2023 Jungyeol Hong et al.
• Authors:
  • Hong, Jungyeol
  • Na, Jieun
  • Kang, Youjeong
  • Kim, Dongho
• Publication Date: 2023-8

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: Article ID 9586307
• Serial:
  • Journal of Advanced Transportation
  • Volume: 2023
  • Publisher: John Wiley & Sons, Incorporated
  • ISSN: 0197-6729
  • EISSN: 2042-3195
  • Serial URL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2042-3195
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Data analysis; Economic development; Forecasting; Population; Regression analysis; Time series analysis
• Geographic Terms: Ulsan (Korea)
• Subject Areas: Economics; Transportation (General);

Filing Info

• Accession Number: 01893493
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 19 2023 9:27AM