Forecasting Foreign Direct Investment Inflow to Egypt and Determinates: Using Machine Learning Algorithms and ARIMA Model
This study aims to determine the primary determination of FDI inflow to Egypt using machine learning algorithms and the ARIMA model and get an accurate prediction of FDI inflow to Egypt during the current decade (2020-2030) and approved that the gradient boosting model is the most accurate algorithm...
出版年: | Journal of Advanced Transportation |
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フォーマット: | Retracted |
言語: | English |
出版事項: |
Hindawi Limited
2021
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オンライン・アクセス: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122189807&doi=10.1155%2f2021%2f9614101&partnerID=40&md5=24b948c9d666e15cf5f9bd481166f484 |
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Abd El-Aal M.F.; Algarni A.; Fayomi A.; Abdul Rahman R.; Alrashidi K. |
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Abd El-Aal M.F.; Algarni A.; Fayomi A.; Abdul Rahman R.; Alrashidi K. 2-s2.0-85122189807 Forecasting Foreign Direct Investment Inflow to Egypt and Determinates: Using Machine Learning Algorithms and ARIMA Model 2021 Journal of Advanced Transportation 2021 10.1155/2021/9614101 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122189807&doi=10.1155%2f2021%2f9614101&partnerID=40&md5=24b948c9d666e15cf5f9bd481166f484 This study aims to determine the primary determination of FDI inflow to Egypt using machine learning algorithms and the ARIMA model and get an accurate prediction of FDI inflow to Egypt during the current decade (2020-2030) and approved that the gradient boosting model is the most accurate algorithms. Also, we find stability in economic indicators in Egypt during the current decade using the ARIMA model. The last step approved that the primary determinant of FDI inflow to Egypt is the Human Development Index, followed by population size, gross domestic product per capita, lending rate, and gross domestic product value. © 2021 Mohamed F. Abd El-Aal et al. Hindawi Limited 1976729 English Retracted All Open Access; Gold Open Access |
author |
2-s2.0-85122189807 |
spellingShingle |
2-s2.0-85122189807 Forecasting Foreign Direct Investment Inflow to Egypt and Determinates: Using Machine Learning Algorithms and ARIMA Model |
author_facet |
2-s2.0-85122189807 |
author_sort |
2-s2.0-85122189807 |
title |
Forecasting Foreign Direct Investment Inflow to Egypt and Determinates: Using Machine Learning Algorithms and ARIMA Model |
title_short |
Forecasting Foreign Direct Investment Inflow to Egypt and Determinates: Using Machine Learning Algorithms and ARIMA Model |
title_full |
Forecasting Foreign Direct Investment Inflow to Egypt and Determinates: Using Machine Learning Algorithms and ARIMA Model |
title_fullStr |
Forecasting Foreign Direct Investment Inflow to Egypt and Determinates: Using Machine Learning Algorithms and ARIMA Model |
title_full_unstemmed |
Forecasting Foreign Direct Investment Inflow to Egypt and Determinates: Using Machine Learning Algorithms and ARIMA Model |
title_sort |
Forecasting Foreign Direct Investment Inflow to Egypt and Determinates: Using Machine Learning Algorithms and ARIMA Model |
publishDate |
2021 |
container_title |
Journal of Advanced Transportation |
container_volume |
2021 |
container_issue |
|
doi_str_mv |
10.1155/2021/9614101 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122189807&doi=10.1155%2f2021%2f9614101&partnerID=40&md5=24b948c9d666e15cf5f9bd481166f484 |
description |
This study aims to determine the primary determination of FDI inflow to Egypt using machine learning algorithms and the ARIMA model and get an accurate prediction of FDI inflow to Egypt during the current decade (2020-2030) and approved that the gradient boosting model is the most accurate algorithms. Also, we find stability in economic indicators in Egypt during the current decade using the ARIMA model. The last step approved that the primary determinant of FDI inflow to Egypt is the Human Development Index, followed by population size, gross domestic product per capita, lending rate, and gross domestic product value. © 2021 Mohamed F. Abd El-Aal et al. |
publisher |
Hindawi Limited |
issn |
1976729 |
language |
English |
format |
Retracted |
accesstype |
All Open Access; Gold Open Access |
record_format |
scopus |
collection |
Scopus |
_version_ |
1828987871111938048 |