AI-Powered Trade Forecasting: A Data-Driven Approach to Saudi Arabia’s Non-Oil Exports
This paper investigates the application of artificial intelligence (AI) in forecasting Saudi Arabia’s non-oil export trajectories, contributing to the Kingdom’s Vision 2030 objectives for economic diversification. A suite of machine learning models, including LSTM, Transformer variants, Ensemble Sta...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/9/4/94 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850183310535294976 |
|---|---|
| author | Musab Aloudah Mahdi Alajmi Alaa Sagheer Abdulelah Algosaibi Badr Almarri Eid Albelwi |
| author_facet | Musab Aloudah Mahdi Alajmi Alaa Sagheer Abdulelah Algosaibi Badr Almarri Eid Albelwi |
| author_sort | Musab Aloudah |
| collection | DOAJ |
| description | This paper investigates the application of artificial intelligence (AI) in forecasting Saudi Arabia’s non-oil export trajectories, contributing to the Kingdom’s Vision 2030 objectives for economic diversification. A suite of machine learning models, including LSTM, Transformer variants, Ensemble Stacking, XGBRegressor, and Random Forest, was applied to historical export and GDP data. Among them, the Advanced Transformer model, configured with an increased attention head size, achieved the highest accuracy (MAPE: 0.73%), effectively capturing complex temporal dependencies. The Non-Linear Blending Ensemble, integrating Random Forest, XGBRegressor, and AdaBoost, also performed robustly (MAPE: 1.23%), demonstrating the benefit of leveraging heterogeneous learners. While the Temporal Fusion Transformer (TFT) provided a useful macroeconomic context through GDP integration, its relatively higher error (MAPE: 5.48%) highlighted the challenges of incorporating aggregate indicators into forecasting pipelines. Explainable AI tools, including SHAP analysis and Partial Dependence Plots (PDPs), revealed that recent export lags (lag1, lag2, lag3, and lag10) were the most influential features, offering critical transparency into model behavior. These findings reinforce the promise of interpretable AI-powered forecasting frameworks in delivering actionable, data-informed insights to support strategic economic planning. |
| format | Article |
| id | doaj-art-cd8bf85af29d4d3e8c649543b65165cd |
| institution | OA Journals |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-cd8bf85af29d4d3e8c649543b65165cd2025-08-20T02:17:24ZengMDPI AGBig Data and Cognitive Computing2504-22892025-04-01949410.3390/bdcc9040094AI-Powered Trade Forecasting: A Data-Driven Approach to Saudi Arabia’s Non-Oil ExportsMusab Aloudah0Mahdi Alajmi1Alaa Sagheer2Abdulelah Algosaibi3Badr Almarri4Eid Albelwi5College of Computer Science and Information Technology, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi ArabiaCollege of Computer Science and Information Technology, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi ArabiaCollege of Computer Science and Information Technology, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi ArabiaCollege of Computer Science and Information Technology, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi ArabiaCollege of Computer Science and Information Technology, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi ArabiaCollege of Computer Science and Information Technology, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi ArabiaThis paper investigates the application of artificial intelligence (AI) in forecasting Saudi Arabia’s non-oil export trajectories, contributing to the Kingdom’s Vision 2030 objectives for economic diversification. A suite of machine learning models, including LSTM, Transformer variants, Ensemble Stacking, XGBRegressor, and Random Forest, was applied to historical export and GDP data. Among them, the Advanced Transformer model, configured with an increased attention head size, achieved the highest accuracy (MAPE: 0.73%), effectively capturing complex temporal dependencies. The Non-Linear Blending Ensemble, integrating Random Forest, XGBRegressor, and AdaBoost, also performed robustly (MAPE: 1.23%), demonstrating the benefit of leveraging heterogeneous learners. While the Temporal Fusion Transformer (TFT) provided a useful macroeconomic context through GDP integration, its relatively higher error (MAPE: 5.48%) highlighted the challenges of incorporating aggregate indicators into forecasting pipelines. Explainable AI tools, including SHAP analysis and Partial Dependence Plots (PDPs), revealed that recent export lags (lag1, lag2, lag3, and lag10) were the most influential features, offering critical transparency into model behavior. These findings reinforce the promise of interpretable AI-powered forecasting frameworks in delivering actionable, data-informed insights to support strategic economic planning.https://www.mdpi.com/2504-2289/9/4/94artificial intelligencemachine learningeconomic forecastingnon-oil exportstime-series forecastingTransformer models |
| spellingShingle | Musab Aloudah Mahdi Alajmi Alaa Sagheer Abdulelah Algosaibi Badr Almarri Eid Albelwi AI-Powered Trade Forecasting: A Data-Driven Approach to Saudi Arabia’s Non-Oil Exports Big Data and Cognitive Computing artificial intelligence machine learning economic forecasting non-oil exports time-series forecasting Transformer models |
| title | AI-Powered Trade Forecasting: A Data-Driven Approach to Saudi Arabia’s Non-Oil Exports |
| title_full | AI-Powered Trade Forecasting: A Data-Driven Approach to Saudi Arabia’s Non-Oil Exports |
| title_fullStr | AI-Powered Trade Forecasting: A Data-Driven Approach to Saudi Arabia’s Non-Oil Exports |
| title_full_unstemmed | AI-Powered Trade Forecasting: A Data-Driven Approach to Saudi Arabia’s Non-Oil Exports |
| title_short | AI-Powered Trade Forecasting: A Data-Driven Approach to Saudi Arabia’s Non-Oil Exports |
| title_sort | ai powered trade forecasting a data driven approach to saudi arabia s non oil exports |
| topic | artificial intelligence machine learning economic forecasting non-oil exports time-series forecasting Transformer models |
| url | https://www.mdpi.com/2504-2289/9/4/94 |
| work_keys_str_mv | AT musabaloudah aipoweredtradeforecastingadatadrivenapproachtosaudiarabiasnonoilexports AT mahdialajmi aipoweredtradeforecastingadatadrivenapproachtosaudiarabiasnonoilexports AT alaasagheer aipoweredtradeforecastingadatadrivenapproachtosaudiarabiasnonoilexports AT abdulelahalgosaibi aipoweredtradeforecastingadatadrivenapproachtosaudiarabiasnonoilexports AT badralmarri aipoweredtradeforecastingadatadrivenapproachtosaudiarabiasnonoilexports AT eidalbelwi aipoweredtradeforecastingadatadrivenapproachtosaudiarabiasnonoilexports |