A Comprehensive Review of Swarm-Based Meta-Heuristic Algorithms for Optimizing LSTM Networks in Time Series Forecasting
Time series forecasting, a critical task in various domains, has seen significant advancements with the emergence of deep learning models like Long Short-Term Memory (LSTM) networks. These models excel at capturing temporal dependencies within data, but their performance is highly sensitive to hyper...
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Hamedon S.N.; Johari J.; Ruslan F.A. |
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Hamedon S.N.; Johari J.; Ruslan F.A. 2-s2.0-85219566544 A Comprehensive Review of Swarm-Based Meta-Heuristic Algorithms for Optimizing LSTM Networks in Time Series Forecasting 2024 2024 IEEE 22nd Student Conference on Research and Development, SCOReD 2024 10.1109/SCOReD64708.2024.10872714 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219566544&doi=10.1109%2fSCOReD64708.2024.10872714&partnerID=40&md5=db119bc7faddf60dcc79a8f7307e1a73 Time series forecasting, a critical task in various domains, has seen significant advancements with the emergence of deep learning models like Long Short-Term Memory (LSTM) networks. These models excel at capturing temporal dependencies within data, but their performance is highly sensitive to hyperparameter optimization. Swarmbased meta-heuristic algorithms, inspired by the collective intelligence of natural systems, offer a promising approach to fine-tune LSTM architectures and parameters. This paper delves into several application of swarm-based meta-heuristic algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Grey Wolf Optimizer (GWO) to enhance LSTM-based time series forecasting. These algorithms efficiently explore vast parameter spaces, identifying optimal configurations that improve prediction accuracy. While these techniques have shown remarkable success, challenges such as computational cost issues remain. Future research should focus on addressing these limitations and developing more sophisticated hybrid approaches to further elevate the performance of LSTM networks in time series forecasting. © 2024 IEEE. Institute of Electrical and Electronics Engineers Inc. English Conference paper |
| author |
2-s2.0-85219566544 |
| spellingShingle |
2-s2.0-85219566544 A Comprehensive Review of Swarm-Based Meta-Heuristic Algorithms for Optimizing LSTM Networks in Time Series Forecasting |
| author_facet |
2-s2.0-85219566544 |
| author_sort |
2-s2.0-85219566544 |
| title |
A Comprehensive Review of Swarm-Based Meta-Heuristic Algorithms for Optimizing LSTM Networks in Time Series Forecasting |
| title_short |
A Comprehensive Review of Swarm-Based Meta-Heuristic Algorithms for Optimizing LSTM Networks in Time Series Forecasting |
| title_full |
A Comprehensive Review of Swarm-Based Meta-Heuristic Algorithms for Optimizing LSTM Networks in Time Series Forecasting |
| title_fullStr |
A Comprehensive Review of Swarm-Based Meta-Heuristic Algorithms for Optimizing LSTM Networks in Time Series Forecasting |
| title_full_unstemmed |
A Comprehensive Review of Swarm-Based Meta-Heuristic Algorithms for Optimizing LSTM Networks in Time Series Forecasting |
| title_sort |
A Comprehensive Review of Swarm-Based Meta-Heuristic Algorithms for Optimizing LSTM Networks in Time Series Forecasting |
| publishDate |
2024 |
| container_title |
2024 IEEE 22nd Student Conference on Research and Development, SCOReD 2024 |
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| doi_str_mv |
10.1109/SCOReD64708.2024.10872714 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85219566544&doi=10.1109%2fSCOReD64708.2024.10872714&partnerID=40&md5=db119bc7faddf60dcc79a8f7307e1a73 |
| description |
Time series forecasting, a critical task in various domains, has seen significant advancements with the emergence of deep learning models like Long Short-Term Memory (LSTM) networks. These models excel at capturing temporal dependencies within data, but their performance is highly sensitive to hyperparameter optimization. Swarmbased meta-heuristic algorithms, inspired by the collective intelligence of natural systems, offer a promising approach to fine-tune LSTM architectures and parameters. This paper delves into several application of swarm-based meta-heuristic algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Grey Wolf Optimizer (GWO) to enhance LSTM-based time series forecasting. These algorithms efficiently explore vast parameter spaces, identifying optimal configurations that improve prediction accuracy. While these techniques have shown remarkable success, challenges such as computational cost issues remain. Future research should focus on addressing these limitations and developing more sophisticated hybrid approaches to further elevate the performance of LSTM networks in time series forecasting. © 2024 IEEE. |
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Institute of Electrical and Electronics Engineers Inc. |
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| language |
English |
| format |
Conference paper |
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scopus |
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Scopus |
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1828987861646442496 |