2025, Vol. 6, Issue 1, Part C
Advanced forecasting of Iraq’s GDP growth using Ai-driven time series models: A comparative analysis
Author(s): Youbert Youel ELIYA
Abstract: Artificial intelligence technologies combined with machine learning algorithms allow the predictive technique to generate forecasts about Iraq's national economy trends. Rising GDP serves as the key indicator which suggests that developing countries are about to experience economic transformation.The volatile oil market combined with random executive decisions together with continuous bureaucratic hiccups make it hard for analysts to apply JsonRequestian forecasting methods to the Iraqi economy. A unique economic climate now exists in Iraq because many economic variables have connected mutually. An evaluation of different AI algorithm models was conducted through GDP dataset analysis from 1990 to 2024. The application system has been designed with XGBoost components combined through Prophet structures and LSTM components. Rearranging decision trees with gradient-boosted capabilities into LSTM neural networks produced a 37% MAPE along with a 42% directional accuracy throughout the entire study period. Multiple models developed by artificial intelligence improve performance results above traditional statistical methods when dealing with major data problems. Post-conflict settings depend heavily on resource-based economic policies according to study results because they supply vital data instruments which enable evidence-based decision making. Market predicting accuracy achieved its strongest gains through specific elements detected by the study during financial trouble periods.
DOI: https://doi.org/10.22271/math.2025.v6.i1c.200
Pages: 240-248 | Views: 98 | Downloads: 53
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How to cite this article:
Youbert Youel ELIYA. Advanced forecasting of Iraq’s GDP growth using Ai-driven time series models: A comparative analysis. Journal of Mathematical Problems, Equations and Statistics. 2025; 6(1): 240-248. DOI: 10.22271/math.2025.v6.i1c.200
