A Hybrid AI Framework for Enhanced Stock Movement Prediction: Integrating ARIMA, RNN, and LightGBM Models
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 inn...
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| Format: | Article |
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MDPI AG
2025-02-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/13/3/162 |
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| author | Adel Alarbi Wagdi Khalifa Ahmad Alzubi |
| author_facet | Adel Alarbi Wagdi Khalifa Ahmad Alzubi |
| author_sort | Adel Alarbi |
| collection | DOAJ |
| description | 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’s 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. |
| format | Article |
| id | doaj-art-aed27725c6b644b09a88aa73cb8008ac |
| institution | Kabale University |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-aed27725c6b644b09a88aa73cb8008ac2025-08-20T03:44:00ZengMDPI AGSystems2079-89542025-02-0113316210.3390/systems13030162A Hybrid AI Framework for Enhanced Stock Movement Prediction: Integrating ARIMA, RNN, and LightGBM ModelsAdel Alarbi0Wagdi Khalifa1Ahmad Alzubi2Institute of Graduate Research and Studies, University of Mediterranean Karpasia, 33010 Mersin, TurkeyInstitute of Graduate Research and Studies, University of Mediterranean Karpasia, 33010 Mersin, TurkeyInstitute of Graduate Research and Studies, University of Mediterranean Karpasia, 33010 Mersin, TurkeyForecasting 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’s 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.https://www.mdpi.com/2079-8954/13/3/162stock market forecastinghybrid AI modelsARIMA-RNN-LightGBM ensembletechnical indicators analysisfinancial time series prediction |
| spellingShingle | Adel Alarbi Wagdi Khalifa Ahmad Alzubi A Hybrid AI Framework for Enhanced Stock Movement Prediction: Integrating ARIMA, RNN, and LightGBM Models Systems stock market forecasting hybrid AI models ARIMA-RNN-LightGBM ensemble technical indicators analysis financial time series prediction |
| title | A Hybrid AI Framework for Enhanced Stock Movement Prediction: Integrating ARIMA, RNN, and LightGBM Models |
| title_full | A Hybrid AI Framework for Enhanced Stock Movement Prediction: Integrating ARIMA, RNN, and LightGBM Models |
| title_fullStr | A Hybrid AI Framework for Enhanced Stock Movement Prediction: Integrating ARIMA, RNN, and LightGBM Models |
| title_full_unstemmed | A Hybrid AI Framework for Enhanced Stock Movement Prediction: Integrating ARIMA, RNN, and LightGBM Models |
| title_short | A Hybrid AI Framework for Enhanced Stock Movement Prediction: Integrating ARIMA, RNN, and LightGBM Models |
| title_sort | hybrid ai framework for enhanced stock movement prediction integrating arima rnn and lightgbm models |
| topic | stock market forecasting hybrid AI models ARIMA-RNN-LightGBM ensemble technical indicators analysis financial time series prediction |
| url | https://www.mdpi.com/2079-8954/13/3/162 |
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