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, i...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/9586307 |
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| _version_ | 1832555327556419584 |
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| author | Jungyeol Hong Jieun Na Youjeong Kang Dongho Kim |
| author_facet | Jungyeol Hong Jieun Na Youjeong Kang Dongho Kim |
| author_sort | Jungyeol Hong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-090dbb1767414571a8884f426706191a |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-090dbb1767414571a8884f426706191a2025-02-03T05:48:30ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/9586307Time Series Forecasting for Regional Development Composite Index Using Real-Time Floating Population DataJungyeol Hong0Jieun Na1Youjeong Kang2Dongho Kim3Department of Transportation EngineeringDepartment of Transportation EngineeringDepartment of Transportation EngineeringDepartment of Big Data Platform and Data Economy ResearchComposite 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.http://dx.doi.org/10.1155/2023/9586307 |
| spellingShingle | Jungyeol Hong Jieun Na Youjeong Kang Dongho Kim Time Series Forecasting for Regional Development Composite Index Using Real-Time Floating Population Data Journal of Advanced Transportation |
| title | Time Series Forecasting for Regional Development Composite Index Using Real-Time Floating Population Data |
| title_full | Time Series Forecasting for Regional Development Composite Index Using Real-Time Floating Population Data |
| title_fullStr | Time Series Forecasting for Regional Development Composite Index Using Real-Time Floating Population Data |
| title_full_unstemmed | Time Series Forecasting for Regional Development Composite Index Using Real-Time Floating Population Data |
| title_short | Time Series Forecasting for Regional Development Composite Index Using Real-Time Floating Population Data |
| title_sort | time series forecasting for regional development composite index using real time floating population data |
| url | http://dx.doi.org/10.1155/2023/9586307 |
| work_keys_str_mv | AT jungyeolhong timeseriesforecastingforregionaldevelopmentcompositeindexusingrealtimefloatingpopulationdata AT jieunna timeseriesforecastingforregionaldevelopmentcompositeindexusingrealtimefloatingpopulationdata AT youjeongkang timeseriesforecastingforregionaldevelopmentcompositeindexusingrealtimefloatingpopulationdata AT donghokim timeseriesforecastingforregionaldevelopmentcompositeindexusingrealtimefloatingpopulationdata |