{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:08:05Z","timestamp":1760058485481,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>This paper investigates the application of artificial intelligence (AI) in forecasting Saudi Arabia\u2019s non-oil export trajectories, contributing to the Kingdom\u2019s 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.<\/jats:p>","DOI":"10.3390\/bdcc9040094","type":"journal-article","created":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T11:26:41Z","timestamp":1744284401000},"page":"94","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Powered Trade Forecasting: A Data-Driven Approach to Saudi Arabia\u2019s Non-Oil Exports"],"prefix":"10.3390","volume":"9","author":[{"given":"Musab","family":"Aloudah","sequence":"first","affiliation":[{"name":"College of Computer Science and Information Technology, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia"}]},{"given":"Mahdi","family":"Alajmi","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Technology, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1599-9286","authenticated-orcid":false,"given":"Alaa","family":"Sagheer","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Technology, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7680-4265","authenticated-orcid":false,"given":"Abdulelah","family":"Algosaibi","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Technology, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia"}]},{"given":"Badr","family":"Almarri","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Technology, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4872-4932","authenticated-orcid":false,"given":"Eid","family":"Albelwi","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Technology, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hasanov, F.J., Javid, M., and Joutz, F.L. (2022). Saudi non-oil exports before and after covid-19: Historical impacts of determinants and scenario analysis. Sustainability, 14.","DOI":"10.3390\/su14042379"},{"key":"ref_2","unstructured":"(2025, March 30). Available online: https:\/\/www.vision2030.gov.sa\/ar\/overview."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"112891","DOI":"10.1109\/ACCESS.2023.3323574","article-title":"Harnessing big data analytics for healthcare: A comprehensive review of frameworks, implications, applications, and impacts","volume":"11","author":"Ahmed","year":"2023","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5132","DOI":"10.1109\/ACCESS.2024.3349495","article-title":"Advancing aviation safety through machine learning and psychophysiological data: A systematic review","volume":"12","author":"Alreshidi","year":"2024","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"72713","DOI":"10.1109\/ACCESS.2020.2988120","article-title":"Sampling for big data profiling: A survey","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jarrah, M., and Derbali, M. (2023). Predicting saudi stock market index by using multivariate time series based on deep learning. Appl. Sci., 13.","DOI":"10.20944\/preprints202306.1537.v1"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yoo, T.-W., and Oh, I.-S. (2020). Time series forecasting of agricultural products\u2019 sales volumes based on seasonal long short-term memory. Appl. Sci., 10.","DOI":"10.3390\/app10228169"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.procs.2021.01.031","article-title":"Forecasting Indonesia exports using a hybrid model arima-lstm","volume":"179","author":"Dave","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"124715","DOI":"10.1109\/ACCESS.2022.3224938","article-title":"Profit prediction using arima, sarima and lstm models in time series forecasting: A comparison","volume":"10","author":"Sirisha","year":"2022","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"117275","DOI":"10.1016\/j.eswa.2022.117275","article-title":"A multi head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks","volume":"202","author":"Reza","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Islam, M., Shuvo, S.S., Shohan, J.A., and Faruque, O. (2023, January 15\u201317). Forecasting of pv plant output using interpretable temporal fusion transformer model. Proceedings of the 2023 North American Power Symposium (NAPS), Asheville, NC, USA.","DOI":"10.1109\/NAPS58826.2023.10318698"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hasan, M., Abedin, M.Z., Hajek, P., Coussement, K., Sultan, M.N., and Lucey, B. (2024). A blending ensemble learning model for crude oil price forecasting. Ann. Oper. Res., 1\u201331.","DOI":"10.1007\/s10479-023-05810-8"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pavlyshenko, B. (2018, January 21\u201325). Using stacking approaches for machine learning models. Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine.","DOI":"10.1109\/DSMP.2018.8478522"},{"key":"ref_14","unstructured":"Carta, S., Podda, A.S., Reforgiato Recupero, D., and Stanciu, M.M. (2021). Explainable ai for financial forecasting. International Conference on Machine Learning, Optimization, and Data Science, 7th International Conference, LOD 2021, Grasmere, UK, October 4\u20138, 2021, Revised Selected Papers, Part II, Springer."},{"key":"ref_15","unstructured":"e Silva, L.C., de Freitas Fonseca, G., Andre, P., and Castro, L. (2024, January 14\u201317). Transformers and attention-based networks in quantitative trading: A comprehensive survey. Proceedings of the 5th ACM International Conference on AI in Finance (ICAIF \u203224), Brooklyn, NY, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1016\/j.ijforecast.2021.02.008","article-title":"A comparison of monthly global indicators for forecasting growth","volume":"37","author":"Baumeister","year":"2021","journal-title":"Int. J. Forecast."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1007\/s10044-021-00980-2","article-title":"A new method of hybrid time window embedding with transformer-based traffic data classification in iot-networked environment","volume":"24","author":"Kozik","year":"2021","journal-title":"Pattern Anal. Appl."},{"key":"ref_18","unstructured":"(2025, March 30). Exports Value by Harmonized System. Available online: https:\/\/datasource.kapsarc.org\/explore\/dataset\/exports-value-by-harmonized-system\/information\/?disjunctive.hs_section&disjunctive.hs_chapter&sort=time_period."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jitsakul, W., and Whasphuttisit, J. (2022, January 19\u201320). Forecasting the export value of smes using time series analysis. Proceedings of the 2022 7th International Conference on Business and Industrial Research (ICBIR), Bangkok, Thailand.","DOI":"10.1109\/ICBIR54589.2022.9786484"},{"key":"ref_20","first-page":"118","article-title":"Assessing the progress of exports diversification in saudi arabia: Growth-share matrix approach","volume":"18","author":"Haque","year":"2020","journal-title":"Probl. Perspect. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"107528","DOI":"10.1016\/j.petrol.2020.107528","article-title":"A new artificial intelligence gannats model predicts gasoline demand of saudi arabia","volume":"194","year":"2020","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_22","unstructured":"(2025, March 30). Gross Domestic Product by Kind of Economic Activity at Current Prices Quarterly. Available online: https:\/\/datasource.kapsarc.org\/explore\/dataset\/saudi-arabia-gross-domestic-product-by-kind-of-economic-activity-at-current-pric\/information\/\/."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Makridakis, S., Spiliotis, E., and Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0194889"},{"key":"ref_24","unstructured":"Batarseh, F., Gopinath, M., Nalluru, G., and Beckman, J. (2019). Application of machine learning in forecasting international trade trends. arXiv."},{"key":"ref_25","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is All you Need. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1007\/s10618-023-00948-2","article-title":"Improving position encoding of transformers for multivariate time series classification","volume":"38","author":"Foumani","year":"2024","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Peng, B., Alcaide, E., Anthony, Q., Albalak, A., Arcadinho, S., Biderman, S., Cao, H., Cheng, X., Chung, M., and Grella, M. (2023). RWKV: Reinventing RNNs for the Transformer Era. Findings of the Association for Computational Linguistics: EMNLP, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2023.findings-emnlp.936"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.neucom.2018.09.082","article-title":"Time Series Forecasting of Petroleum Production using Deep LSTM Recurrent Networks","volume":"323","author":"Sagheer","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1016\/j.ijforecast.2021.03.012","article-title":"Temporal Fusion Transformers for interpretable multi-horizon time series forecasting","volume":"37","author":"Lim","year":"2021","journal-title":"Int. J. Forecast."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chatzimparmpas, A., Martins, R.M., Kucher, K., and Kerren, A. (2021, January 26\u201328). Empirical Study: Visual Analytics for Comparing Stacking to Blending Ensemble Learning. Proceedings of the 2021 23rd International Conference on Control Systems and Computer Science (CSCS), Bucharest, Romania.","DOI":"10.1109\/CSCS52396.2021.00008"},{"key":"ref_33","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017, January 4\u20139). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS\u203217), Long Beach, CA, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hu, Y., Wei, R., Yang, Y., Li, X., Huang, Z., Liu, Y., He, C., and Lu, H. (2022). Performance Degradation Prediction Using LSTM with Optimized Parameters. Sensors, 22.","DOI":"10.3390\/s22062407"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Maccarrone, G., Morelli, G., and Spadaccini, S. (2021). GDP Forecasting: Machine Learning, Linear or Autoregression?. Front. Artif. Intell., 4.","DOI":"10.3389\/frai.2021.757864"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/4\/94\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:13:17Z","timestamp":1760029997000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/4\/94"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,9]]},"references-count":35,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["bdcc9040094"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9040094","relation":{},"ISSN":["2504-2289"],"issn-type":[{"type":"electronic","value":"2504-2289"}],"subject":[],"published":{"date-parts":[[2025,4,9]]}}}