Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy.
Stock price prediction is a challenging research domain. The long short-term memory neural network (LSTM) widely employed in stock price prediction due to its ability to address long-term dependence and transmission of historical time signals in time series data. However, manual tuning of LSTM param...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0310296 |
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| _version_ | 1832540226182971392 |
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| author | Xiaohua Zeng Changzhou Liang Qian Yang Fei Wang Jieping Cai |
| author_facet | Xiaohua Zeng Changzhou Liang Qian Yang Fei Wang Jieping Cai |
| author_sort | Xiaohua Zeng |
| collection | DOAJ |
| description | Stock price prediction is a challenging research domain. The long short-term memory neural network (LSTM) widely employed in stock price prediction due to its ability to address long-term dependence and transmission of historical time signals in time series data. However, manual tuning of LSTM parameters significantly impacts model performance. PSO-LSTM model leveraging PSO's efficient swarm intelligence and strong optimization capabilities is proposed in this article. The experimental results on six global stock indices demonstrate that PSO-LSTM effectively fits real data, achieving high prediction accuracy. Moreover, increasing PSO iterations lead to gradual loss reduction, which indicates PSO-LSTM's good convergence. Comparative analysis with seven other machine learning algorithms confirms the superior performance of PSO-LSTM. Furthermore, the impact of different retrospective periods on prediction accuracy and finding consistent results across varying time spans are. Conducted in the experiments. |
| format | Article |
| id | doaj-art-0430e844f0b14892ad4504e8025a9156 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-0430e844f0b14892ad4504e8025a91562025-02-05T05:31:27ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031029610.1371/journal.pone.0310296Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy.Xiaohua ZengChangzhou LiangQian YangFei WangJieping CaiStock price prediction is a challenging research domain. The long short-term memory neural network (LSTM) widely employed in stock price prediction due to its ability to address long-term dependence and transmission of historical time signals in time series data. However, manual tuning of LSTM parameters significantly impacts model performance. PSO-LSTM model leveraging PSO's efficient swarm intelligence and strong optimization capabilities is proposed in this article. The experimental results on six global stock indices demonstrate that PSO-LSTM effectively fits real data, achieving high prediction accuracy. Moreover, increasing PSO iterations lead to gradual loss reduction, which indicates PSO-LSTM's good convergence. Comparative analysis with seven other machine learning algorithms confirms the superior performance of PSO-LSTM. Furthermore, the impact of different retrospective periods on prediction accuracy and finding consistent results across varying time spans are. Conducted in the experiments.https://doi.org/10.1371/journal.pone.0310296 |
| spellingShingle | Xiaohua Zeng Changzhou Liang Qian Yang Fei Wang Jieping Cai Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy. PLoS ONE |
| title | Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy. |
| title_full | Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy. |
| title_fullStr | Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy. |
| title_full_unstemmed | Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy. |
| title_short | Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy. |
| title_sort | enhancing stock index prediction a hybrid lstm pso model for improved forecasting accuracy |
| url | https://doi.org/10.1371/journal.pone.0310296 |
| work_keys_str_mv | AT xiaohuazeng enhancingstockindexpredictionahybridlstmpsomodelforimprovedforecastingaccuracy AT changzhouliang enhancingstockindexpredictionahybridlstmpsomodelforimprovedforecastingaccuracy AT qianyang enhancingstockindexpredictionahybridlstmpsomodelforimprovedforecastingaccuracy AT feiwang enhancingstockindexpredictionahybridlstmpsomodelforimprovedforecastingaccuracy AT jiepingcai enhancingstockindexpredictionahybridlstmpsomodelforimprovedforecastingaccuracy |