A Hybrid LSTM-GRU Model for Stock Price Prediction
The dynamic variation of the stock market plays a crucial role in assessing a country’s economic power and development. Modeling the chaotic fluctuations in stock prices aids investors and traders in uncertain situations by evaluating market trends for investment decisions. Previous metho...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11072109/ |
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| author | Amirfarhad Farhadi Azadeh Zamanifar Amir Alipour Alireza Taheri Mohammad Asadolahi |
| author_facet | Amirfarhad Farhadi Azadeh Zamanifar Amir Alipour Alireza Taheri Mohammad Asadolahi |
| author_sort | Amirfarhad Farhadi |
| collection | DOAJ |
| description | The dynamic variation of the stock market plays a crucial role in assessing a country’s economic power and development. Modeling the chaotic fluctuations in stock prices aids investors and traders in uncertain situations by evaluating market trends for investment decisions. Previous methods utilized Recurrent Neural Network (RNN) models for prediction. However, none of the previous works attempted to leverage the full range of technical analysis features and use Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) modules to capture short and long dependencies. Initially, we use machine learning to extract appropriate features for making predictions. Subsequently, the impact of each feature is adjusted proportionally to the weight matrix extracted from the neural network. We combined LSTM and GRU to capture long and short information horizons in stock sequences to mitigate limitations of prior methods, such as the difficulty in hyperparameter tuning. By combining the capabilities of these two networks along together, we present a model for predicting market fluctuations. Our proposed model was evaluated in a specified test market, achieving a 3% improvement in Mean Squared Error (MSE) over the previous best approaches. |
| format | Article |
| id | doaj-art-f6f40e94b15b4635bedc2b0a1683a8fe |
| institution | Matheson Library |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f6f40e94b15b4635bedc2b0a1683a8fe2025-07-11T23:01:13ZengIEEEIEEE Access2169-35362025-01-011311759411761810.1109/ACCESS.2025.358655811072109A Hybrid LSTM-GRU Model for Stock Price PredictionAmirfarhad Farhadi0https://orcid.org/0000-0002-5357-0459Azadeh Zamanifar1https://orcid.org/0000-0002-2629-4794Amir Alipour2https://orcid.org/0009-0006-0298-3057Alireza Taheri3https://orcid.org/0009-0001-9856-0027Mohammad Asadolahi4https://orcid.org/0009-0005-0333-9119Department of Computer Engineering, Science and Research Branch of Islamic Azad University, Tehran, IranDepartment of Computer Engineering, Science and Research Branch of Islamic Azad University, Tehran, IranDepartment of Computer Engineering, Science and Research Branch of Islamic Azad University, Tehran, IranDepartment of Computer Engineering, Science and Research Branch of Islamic Azad University, Tehran, IranDepartment of Computer Engineering, Science and Research Branch of Islamic Azad University, Tehran, IranThe dynamic variation of the stock market plays a crucial role in assessing a country’s economic power and development. Modeling the chaotic fluctuations in stock prices aids investors and traders in uncertain situations by evaluating market trends for investment decisions. Previous methods utilized Recurrent Neural Network (RNN) models for prediction. However, none of the previous works attempted to leverage the full range of technical analysis features and use Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) modules to capture short and long dependencies. Initially, we use machine learning to extract appropriate features for making predictions. Subsequently, the impact of each feature is adjusted proportionally to the weight matrix extracted from the neural network. We combined LSTM and GRU to capture long and short information horizons in stock sequences to mitigate limitations of prior methods, such as the difficulty in hyperparameter tuning. By combining the capabilities of these two networks along together, we present a model for predicting market fluctuations. Our proposed model was evaluated in a specified test market, achieving a 3% improvement in Mean Squared Error (MSE) over the previous best approaches.https://ieeexplore.ieee.org/document/11072109/Stock predictionRNNLSTMGRUhybrid approach |
| spellingShingle | Amirfarhad Farhadi Azadeh Zamanifar Amir Alipour Alireza Taheri Mohammad Asadolahi A Hybrid LSTM-GRU Model for Stock Price Prediction IEEE Access Stock prediction RNN LSTM GRU hybrid approach |
| title | A Hybrid LSTM-GRU Model for Stock Price Prediction |
| title_full | A Hybrid LSTM-GRU Model for Stock Price Prediction |
| title_fullStr | A Hybrid LSTM-GRU Model for Stock Price Prediction |
| title_full_unstemmed | A Hybrid LSTM-GRU Model for Stock Price Prediction |
| title_short | A Hybrid LSTM-GRU Model for Stock Price Prediction |
| title_sort | hybrid lstm gru model for stock price prediction |
| topic | Stock prediction RNN LSTM GRU hybrid approach |
| url | https://ieeexplore.ieee.org/document/11072109/ |
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