{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T11:56:38Z","timestamp":1787313398323,"version":"build-2736575974"},"reference-count":35,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T00:00:00Z","timestamp":1740528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Forecasting stock market movements is a critical yet challenging endeavor due to the inherent nonlinearity, chaotic behavior, and dynamic nature of financial markets. This study proposes the Autoregressive Integrated Moving Average Ensemble Recurrent Light Gradient Boosting Machine (AR-ERLM), an innovative model designed to enhance the precision and reliability of stock movement predictions. The AR-ERLM integrates ARIMA for identifying linear dependencies, RNN for capturing temporal dynamics, and LightGBM for managing large-scale datasets and non-linear relationships. Using datasets from Netflix, Amazon, and Meta platforms, the model incorporates technical indicators and Google Trends data to construct a comprehensive feature space. Experimental results reveal that the AR-ERLM outperforms benchmark models such as GA-XGBoost, Conv-LSTM, and ANN. For the Netflix dataset, the AR-ERLM achieved an RMSE of 2.35, MSE of 5.54, and MAE of 1.58, surpassing other models in minimizing prediction errors. Moreover, the model demonstrates robust adaptability to real-time data and consistently superior performance across multiple metrics. The findings emphasize AR-ERLM\u2019s potential to enhance predictive accuracy, mitigating overfitting and reducing computational overhead. These implications are crucial for financial institutions and investors seeking reliable tools for risk assessment and decision-making. The study sets the foundation for integrating advanced AI models into financial forecasting, encouraging future exploration of hybrid optimization techniques to further refine predictive capabilities.<\/jats:p>","DOI":"10.3390\/systems13030162","type":"journal-article","created":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T11:22:12Z","timestamp":1740568932000},"page":"162","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Hybrid AI Framework for Enhanced Stock Movement Prediction: Integrating ARIMA, RNN, and LightGBM Models"],"prefix":"10.3390","volume":"13","author":[{"given":"Adel","family":"Alarbi","sequence":"first","affiliation":[{"name":"Institute of Graduate Research and Studies, University of Mediterranean Karpasia, 33010 Mersin, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wagdi","family":"Khalifa","sequence":"additional","affiliation":[{"name":"Institute of Graduate Research and Studies, University of Mediterranean Karpasia, 33010 Mersin, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0082-9922","authenticated-orcid":false,"given":"Ahmad","family":"Alzubi","sequence":"additional","affiliation":[{"name":"Institute of Graduate Research and Studies, University of Mediterranean Karpasia, 33010 Mersin, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5844","DOI":"10.1109\/TAI.2024.3408129","article-title":"A Hybrid Relational Approach Towards Stock Price Prediction and Profitability","volume":"5","author":"Patel","year":"2024","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s10287-016-0267-0","article-title":"Volatility forecasting via SVR\u2013GARCH with mixture of Gaussian kernels","volume":"14","author":"Bezerra","year":"2017","journal-title":"Comput. Manag. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3007","DOI":"10.1007\/s10462-019-09754-z","article-title":"A systematic review of fundamental and technical analysis of stock market predictions","volume":"53","author":"Nti","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.eswa.2017.10.023","article-title":"New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic","volume":"94","author":"Ahmadi","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"101433","DOI":"10.1109\/ACCESS.2021.3096825","article-title":"Stock trend prediction using candlestick charting and ensemble machine learning techniques with a novelty feature engineering scheme","volume":"9","author":"Lin","year":"2021","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Saetia, K., and Yokrattanasak, J. (2022). Stock movement prediction using machine learning based on technical indicators and Google trend searches in Thailand. Int. J. Financ. Stud., 11.","DOI":"10.3390\/ijfs11010005"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3433","DOI":"10.1007\/s12652-020-01839-w","article-title":"Stock market prediction using machine learning classifiers and social media, news","volume":"13","author":"Khan","year":"2022","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_8","first-page":"100060","article-title":"Feature selection and deep neural networks for stock price direction forecasting using technical analysis indicators","volume":"5","author":"Peng","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107119","DOI":"10.1016\/j.knosys.2021.107119","article-title":"Technical analysis strategy optimization using a machine learning approach in stock market indices","volume":"225","author":"Ayala","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103328","DOI":"10.1016\/j.ipm.2023.103328","article-title":"Forecasting movements of stock time series based on hidden state guided deep learning approach","volume":"60","author":"Jiang","year":"2023","journal-title":"Inf. Process. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106943","DOI":"10.1016\/j.asoc.2020.106943","article-title":"Mean\u2013variance portfolio optimization using machine learning-based stock price prediction","volume":"100","author":"Chen","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_12","first-page":"3","article-title":"Portfolio selection","volume":"2","author":"Fabozzi","year":"2008","journal-title":"Handb. Financ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"115716","DOI":"10.1016\/j.eswa.2021.115716","article-title":"Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process","volume":"186","author":"Yun","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3519","DOI":"10.1166\/jctn.2019.8317","article-title":"Forecasting of stock price using autoregressive integrated moving average model","volume":"16","author":"Jiang","year":"2019","journal-title":"J. Comput. Theor. Nanosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101554","DOI":"10.1016\/j.ribaf.2021.101554","article-title":"Exploring the predictability of cryptocurrencies via Bayesian hidden Markov models","volume":"59","author":"Koki","year":"2022","journal-title":"Res. Int. Bus. Financ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"115836","DOI":"10.1016\/j.eswa.2021.115836","article-title":"A dynamic predictor selection algorithm for predicting stock market movement","volume":"186","author":"Dong","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"118128","DOI":"10.1016\/j.eswa.2022.118128","article-title":"Stock market index prediction using deep Transformer model","volume":"208","author":"Wang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"121424","DOI":"10.1016\/j.eswa.2023.121424","article-title":"Series decomposition Transformer with period-correlation for stock market index prediction","volume":"237","author":"Tao","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"115879","DOI":"10.1016\/j.eswa.2021.115879","article-title":"COVID19-HPSMP: COVID-19 adopted Hybrid and Parallel deep information fusion framework for stock price movement prediction","volume":"187","author":"Ronaghi","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_20","unstructured":"(2024, May 02). Amazon Dataset. Available online: https:\/\/finance.yahoo.com\/quote\/AMZN."},{"key":"ref_21","unstructured":"(2024, May 02). Meta Dataset. Available online: https:\/\/finance.yahoo.com\/quote\/META."},{"key":"ref_22","unstructured":"(2024, May 02). Netflix Dataset. Available online: https:\/\/finance.yahoo.com\/quote\/NFLX."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ouyang, Y., Li, S., Yao, K., and Wang, J. (2022, January 26\u201328). Analysis of Investment Indicators for the Electronic Components Sector of the A-Share Market. Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022), Guangzhou, China.","DOI":"10.2991\/978-94-6463-030-5_20"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"21","DOI":"10.3126\/irjmmc.v3i4.48859","article-title":"Use of Moving Average Convergence Divergence for Predicting Price Movements","volume":"3","author":"Joshi","year":"2022","journal-title":"Int. Res. J. MMC (IRJMMC)"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"405","DOI":"10.13189\/ujaf.2021.090315","article-title":"Predicting stock market movements using artificial neural networks","volume":"9","author":"Chandrika","year":"2021","journal-title":"Univers. J. Account. Financ."},{"key":"ref_26","first-page":"223","article-title":"The ARIMA Model for the Indonesia Stock Price","volume":"11","author":"Wahyudi","year":"2017","journal-title":"Int. J. Econ. Manag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","article-title":"Time series forecasting using a hybrid ARIMA and neural network model","volume":"50","author":"Zhang","year":"2003","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gan, M., Pan, S., Chen, Y., Cheng, C., Pan, H., and Zhu, X. (2021). Application of the machine learning lightgbm model to the prediction of the water levels of the lower columbia river. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9050496"},{"key":"ref_29","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105009","DOI":"10.1109\/ACCESS.2023.3318478","article-title":"Cost Harmonization LightGBM-Based Stock Market Prediction","volume":"11","author":"Zhao","year":"2023","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Selvin, S., Vinayakumar, R., Gopalakrishnan, E.A., Menon, V.K., and Soman, K.P. (2017, January 13\u201316). Stock price prediction using LSTM, RNN and CNN-sliding window model. Proceedings of the 2017 international conference on advances in computing, communications and informatics (ICACCI), Udupi, India.","DOI":"10.1109\/ICACCI.2017.8126078"},{"key":"ref_32","unstructured":"(2025, February 22). Nifty50 Dataset. Available online: https:\/\/finance.yahoo.com\/quote\/%5ENSEI\/history\/?fr=sycsrp_catchall."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"121824","DOI":"10.1016\/j.ins.2024.121824","article-title":"Stock complex networks based on the GA-LightGBM model: The prediction of firm performance","volume":"700","author":"Huang","year":"2024","journal-title":"Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5379","DOI":"10.1007\/s00521-019-04698-5","article-title":"Stock price forecast based on combined model of ARI-MA-LS-SVM","volume":"32","author":"Xiao","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2906463","DOI":"10.1155\/2021\/2906463","article-title":"Predicting the direction movement of financial time series using artificial neural network and support vector machine","volume":"2021","author":"Ali","year":"2021","journal-title":"Complexity"}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/3\/162\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:43:32Z","timestamp":1760028212000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/3\/162"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,26]]},"references-count":35,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["systems13030162"],"URL":"https:\/\/doi.org\/10.3390\/systems13030162","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,26]]}}}