Based on the Improved PSO-TPA-LSTM Model Chaotic Time Series Prediction
In order to enhance the prediction accuracy and computational efficiency of chaotic sequence data, issues such as gradient explosion and the long computation time of traditional methods need to be addressed. In this paper, an improved Particle Swarm Optimization (PSO) algorithm and Long Short-Term M...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/14/11/1696 |
| _version_ | 1797460163724050432 |
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| author | Zijian Cai Guolin Feng Qiguang Wang |
| author_facet | Zijian Cai Guolin Feng Qiguang Wang |
| author_sort | Zijian Cai |
| collection | DOAJ |
| description | In order to enhance the prediction accuracy and computational efficiency of chaotic sequence data, issues such as gradient explosion and the long computation time of traditional methods need to be addressed. In this paper, an improved Particle Swarm Optimization (PSO) algorithm and Long Short-Term Memory (LSTM) neural network are proposed for chaotic prediction. The temporal pattern attention mechanism (TPA) is introduced to extract the weights and key information of each input feature, ensuring the temporal nature of chaotic historical data. Additionally, the PSO algorithm is employed to optimize the hyperparameters (learning rate, number of iterations) of the LSTM network, resulting in an optimal model for chaotic data prediction. Finally, the validation is conducted using chaotic data generated from three different initial values of the Lorenz system. The root mean square error (RMSE) is reduced by 0.421, the mean absolute error (MAE) is reduced by 0.354, and the coefficient of determination (R<sup>2</sup>) is improved by 0.4. The proposed network demonstrates good adaptability to complex chaotic data, surpassing the accuracy of the LSTM and PSO-LSTM models, thereby achieving higher prediction accuracy. |
| first_indexed | 2024-03-09T17:01:12Z |
| format | Article |
| id | doaj.art-0bc2de59e83947c4bc97892af6cc91a4 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-09T17:01:12Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-0bc2de59e83947c4bc97892af6cc91a42023-11-24T14:28:46ZengMDPI AGAtmosphere2073-44332023-11-011411169610.3390/atmos14111696Based on the Improved PSO-TPA-LSTM Model Chaotic Time Series PredictionZijian Cai0Guolin Feng1Qiguang Wang2College of Physical Science and Technology, Yangzhou University, Yangzhou 225002, ChinaCollege of Physical Science and Technology, Yangzhou University, Yangzhou 225002, ChinaChina Meteorological Administration Training Center, China Meteorological Administration, Beijing 100081, ChinaIn order to enhance the prediction accuracy and computational efficiency of chaotic sequence data, issues such as gradient explosion and the long computation time of traditional methods need to be addressed. In this paper, an improved Particle Swarm Optimization (PSO) algorithm and Long Short-Term Memory (LSTM) neural network are proposed for chaotic prediction. The temporal pattern attention mechanism (TPA) is introduced to extract the weights and key information of each input feature, ensuring the temporal nature of chaotic historical data. Additionally, the PSO algorithm is employed to optimize the hyperparameters (learning rate, number of iterations) of the LSTM network, resulting in an optimal model for chaotic data prediction. Finally, the validation is conducted using chaotic data generated from three different initial values of the Lorenz system. The root mean square error (RMSE) is reduced by 0.421, the mean absolute error (MAE) is reduced by 0.354, and the coefficient of determination (R<sup>2</sup>) is improved by 0.4. The proposed network demonstrates good adaptability to complex chaotic data, surpassing the accuracy of the LSTM and PSO-LSTM models, thereby achieving higher prediction accuracy.https://www.mdpi.com/2073-4433/14/11/1696chaotic sequenceparticle swarm optimization algorithmtime-mode attention mechanismlong short-term memoryLorenz system |
| spellingShingle | Zijian Cai Guolin Feng Qiguang Wang Based on the Improved PSO-TPA-LSTM Model Chaotic Time Series Prediction Atmosphere chaotic sequence particle swarm optimization algorithm time-mode attention mechanism long short-term memory Lorenz system |
| title | Based on the Improved PSO-TPA-LSTM Model Chaotic Time Series Prediction |
| title_full | Based on the Improved PSO-TPA-LSTM Model Chaotic Time Series Prediction |
| title_fullStr | Based on the Improved PSO-TPA-LSTM Model Chaotic Time Series Prediction |
| title_full_unstemmed | Based on the Improved PSO-TPA-LSTM Model Chaotic Time Series Prediction |
| title_short | Based on the Improved PSO-TPA-LSTM Model Chaotic Time Series Prediction |
| title_sort | based on the improved pso tpa lstm model chaotic time series prediction |
| topic | chaotic sequence particle swarm optimization algorithm time-mode attention mechanism long short-term memory Lorenz system |
| url | https://www.mdpi.com/2073-4433/14/11/1696 |
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