GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism
This study introduces GA-Attention-Fuzzy-Stock-Net, a novel hybrid architecture that integrates genetic algorithms, attention mechanisms, and neuro-fuzzy systems for stock market price prediction. The research investigates the effectiveness of different architectural configurations, including variat...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402500773X |
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| author | Burak Gülmez |
| author_facet | Burak Gülmez |
| author_sort | Burak Gülmez |
| collection | DOAJ |
| description | This study introduces GA-Attention-Fuzzy-Stock-Net, a novel hybrid architecture that integrates genetic algorithms, attention mechanisms, and neuro-fuzzy systems for stock market price prediction. The research investigates the effectiveness of different architectural configurations, including variations in fuzzy layer membership functions (triangular, trapezoidal, Gaussian) and neural network architectures (1D ANN, 2D ANN, 1D LSTM, 2D LSTM). The model's performance is evaluated across multiple temporal horizons using sliding windows (5-day, 10-day, 20-day) to capture varying market dynamics. Genetic algorithms optimize the hyperparameters, including learning rates and network architectures, while the attention mechanism enhances the model's ability to focus on relevant temporal patterns. The study utilizes data from major technology stocks. Results demonstrate that GA-Attention-Fuzzy-Stock-Net consistently outperforms traditional machine learning approaches and baseline models across different evaluation metrics (MSE, MAE, MAPE, R2). The findings provide valuable insights for practitioners in financial markets and contribute to the advancement of hybrid intelligent systems for time series prediction. The model's superior performance is attributed to its unique integration of evolutionary optimization, attention-based feature selection, and fuzzy logic's ability to handle uncertainty in financial data. |
| format | Article |
| id | doaj-art-737f272d2090493ea71f719a53196729 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-737f272d2090493ea71f719a531967292025-02-07T04:47:56ZengElsevierHeliyon2405-84402025-02-01113e42393GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanismBurak Gülmez0Department of Industrial Engineering, Mudanya University, Mudanya, Bursa, 16940, Türkiye; Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands; Corresponding author .This study introduces GA-Attention-Fuzzy-Stock-Net, a novel hybrid architecture that integrates genetic algorithms, attention mechanisms, and neuro-fuzzy systems for stock market price prediction. The research investigates the effectiveness of different architectural configurations, including variations in fuzzy layer membership functions (triangular, trapezoidal, Gaussian) and neural network architectures (1D ANN, 2D ANN, 1D LSTM, 2D LSTM). The model's performance is evaluated across multiple temporal horizons using sliding windows (5-day, 10-day, 20-day) to capture varying market dynamics. Genetic algorithms optimize the hyperparameters, including learning rates and network architectures, while the attention mechanism enhances the model's ability to focus on relevant temporal patterns. The study utilizes data from major technology stocks. Results demonstrate that GA-Attention-Fuzzy-Stock-Net consistently outperforms traditional machine learning approaches and baseline models across different evaluation metrics (MSE, MAE, MAPE, R2). The findings provide valuable insights for practitioners in financial markets and contribute to the advancement of hybrid intelligent systems for time series prediction. The model's superior performance is attributed to its unique integration of evolutionary optimization, attention-based feature selection, and fuzzy logic's ability to handle uncertainty in financial data.http://www.sciencedirect.com/science/article/pii/S240584402500773XStock market price predictionFuzzy logicNeuro-Fuzzy systemGenetic algorithmAttention mechanism |
| spellingShingle | Burak Gülmez GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism Heliyon Stock market price prediction Fuzzy logic Neuro-Fuzzy system Genetic algorithm Attention mechanism |
| title | GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism |
| title_full | GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism |
| title_fullStr | GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism |
| title_full_unstemmed | GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism |
| title_short | GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism |
| title_sort | ga attention fuzzy stock net an optimized neuro fuzzy system for stock market price prediction with genetic algorithm and attention mechanism |
| topic | Stock market price prediction Fuzzy logic Neuro-Fuzzy system Genetic algorithm Attention mechanism |
| url | http://www.sciencedirect.com/science/article/pii/S240584402500773X |
| work_keys_str_mv | AT burakgulmez gaattentionfuzzystocknetanoptimizedneurofuzzysystemforstockmarketpricepredictionwithgeneticalgorithmandattentionmechanism |