Forecasting Foreign Direct Investment Inflow to Egypt and Determinates: Using Machine Learning Algorithms and ARIMA Model | Zendy

Mohamed F. Abd El-Aal | Zendy; Ali Algarni | Zendy; Aisha Fayomi | Zendy; RAahayu Abdul Rahman | Zendy; Khudir Alrashidi | Zendy

Open Access

Forecasting Foreign Direct Investment Inflow to Egypt and Determinates: Using Machine Learning Algorithms and ARIMA Model

Author(s) -

Mohamed F. Abd El-Aal,

Ali Algarni,

Aisha Fayomi,

RAahayu Abdul Rahman,

Khudir Alrashidi

Publication year - 2021

Publication title -

journal of advanced transportation

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.577

H-Index - 46

eISSN - 2042-3195

pISSN - 0197-6729

DOI - 10.1155/2021/9614101

Subject(s) - autoregressive integrated moving average , gross domestic product , foreign direct investment , inflow , per capita , boosting (machine learning) , algorithm , population , machine learning , econometrics , mathematics , computer science , economics , time series , geography , meteorology , macroeconomics , demography , sociology

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.

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