A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting
Least Squares Support Vector Machine (LSSVM) has been known to be one of the effective forecasting models. However, its operation relies on two important parameters (regularization and kernel). Pre-determining the values of parameters will affect the results of the forecasting model; hence, to find...
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| Format: | Article |
| Language: | English |
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Universiti Utara Malaysia Press
2020
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| Online Access: | https://repo.uum.edu.my/id/eprint/28795/1/JICT%2019%2003%202020%20351-379.pdf |
| _version_ | 1825805865093955584 |
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| author | Mohammed, Athraa Jasim Ghathwan, Khalil Ibrahim Yusof, Yuhanis |
| author_facet | Mohammed, Athraa Jasim Ghathwan, Khalil Ibrahim Yusof, Yuhanis |
| author_sort | Mohammed, Athraa Jasim |
| collection | UUM |
| description | Least Squares Support Vector Machine (LSSVM) has been known to be one of the effective forecasting models. However, its operation relies on two important parameters (regularization and kernel). Pre-determining the values of parameters will affect the results of the forecasting model; hence, to find the optimal value of these parameters, this study investigates the adaptation of Bat and Cuckoo Search algorithms to optimize LSSVM parameters. Even though Cuckoo Search has been proven to be able to solve global optimization in various areas, the algorithm leads to a slow convergence rate when the step size is large. Hence, to enhance the search ability of Cuckoo Search, it is integrated with Bat algorithm that offers a balanced search between global and local. Evaluation was performed separately to further analyze the strength of Bat and Cuckoo Search to optimize LSSVM parameters. Five evaluation metrics were utilized; mean average percent error (MAPE), accuracy, symmetric mean absolute percent error (SMAPE), root mean square percent error (RMSPE) and fitness value. Experimental results on diabetes forecasting demonstrated that the proposed BAT-LSSVM and CUCKOO-LSSVM generated lower MAPE and SMAPE, at the same time produced higher accuracy and fitness value compared to particle swarm optimization (PSO)-LSSVM and a non-optimized LSSVM. Following the success, this study has integrated the two algorithms to better optimize the LSSVM. The newly proposed forecasting algorithm, termed as CUCKOO-BAT-LSSVM, produces better forecasting in terms of MAPE, accuracy and RMSPE. Such an outcome provides an alternative model to be used in facilitating decision-making in forecasting. |
| first_indexed | 2024-07-04T06:39:12Z |
| format | Article |
| id | uum-28795 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:39:12Z |
| publishDate | 2020 |
| publisher | Universiti Utara Malaysia Press |
| record_format | eprints |
| spelling | uum-287952022-08-07T03:16:22Z https://repo.uum.edu.my/id/eprint/28795/ A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting Mohammed, Athraa Jasim Ghathwan, Khalil Ibrahim Yusof, Yuhanis QA75 Electronic computers. Computer science Least Squares Support Vector Machine (LSSVM) has been known to be one of the effective forecasting models. However, its operation relies on two important parameters (regularization and kernel). Pre-determining the values of parameters will affect the results of the forecasting model; hence, to find the optimal value of these parameters, this study investigates the adaptation of Bat and Cuckoo Search algorithms to optimize LSSVM parameters. Even though Cuckoo Search has been proven to be able to solve global optimization in various areas, the algorithm leads to a slow convergence rate when the step size is large. Hence, to enhance the search ability of Cuckoo Search, it is integrated with Bat algorithm that offers a balanced search between global and local. Evaluation was performed separately to further analyze the strength of Bat and Cuckoo Search to optimize LSSVM parameters. Five evaluation metrics were utilized; mean average percent error (MAPE), accuracy, symmetric mean absolute percent error (SMAPE), root mean square percent error (RMSPE) and fitness value. Experimental results on diabetes forecasting demonstrated that the proposed BAT-LSSVM and CUCKOO-LSSVM generated lower MAPE and SMAPE, at the same time produced higher accuracy and fitness value compared to particle swarm optimization (PSO)-LSSVM and a non-optimized LSSVM. Following the success, this study has integrated the two algorithms to better optimize the LSSVM. The newly proposed forecasting algorithm, termed as CUCKOO-BAT-LSSVM, produces better forecasting in terms of MAPE, accuracy and RMSPE. Such an outcome provides an alternative model to be used in facilitating decision-making in forecasting. Universiti Utara Malaysia Press 2020 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/28795/1/JICT%2019%2003%202020%20351-379.pdf Mohammed, Athraa Jasim and Ghathwan, Khalil Ibrahim and Yusof, Yuhanis (2020) A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting. Journal of Information and Communication Technology, 19 (03). pp. 351-379. ISSN 2180-3862 |
| spellingShingle | QA75 Electronic computers. Computer science Mohammed, Athraa Jasim Ghathwan, Khalil Ibrahim Yusof, Yuhanis A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting |
| title | A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting |
| title_full | A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting |
| title_fullStr | A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting |
| title_full_unstemmed | A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting |
| title_short | A Hybrid Least Squares Support Vector Machine with Bat and Cuckoo Search Algorithms for Time Series Forecasting |
| title_sort | hybrid least squares support vector machine with bat and cuckoo search algorithms for time series forecasting |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/28795/1/JICT%2019%2003%202020%20351-379.pdf |
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