LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting
The importance of optimizing Least Squares Support Vector Machines (LSSVM) embedded control parameters has motivated researchers to search for proficient optimization techniques. In this study, a new metaheuristic algorithm, viz., Grey Wolf Optimizer (GWO), is employed to optimize the parameter...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2015
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/11215/ http://umpir.ump.edu.my/id/eprint/11215/1/LS-SVM%20Hyper-parameters%20Optimization%20based%20on%20GWO%20Algorithm%20for%20Time%20Series%20Forecasting.pdf |
| _version_ | 1848818949980946432 |
|---|---|
| author | Zuriani, Mustaffa Mohd Herwan, Sulaiman M. N. M., Kahar |
| author_facet | Zuriani, Mustaffa Mohd Herwan, Sulaiman M. N. M., Kahar |
| author_sort | Zuriani, Mustaffa |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The importance of optimizing Least Squares
Support Vector Machines (LSSVM) embedded control
parameters has motivated researchers to search for
proficient optimization techniques. In this study, a new
metaheuristic algorithm, viz., Grey Wolf Optimizer
(GWO), is employed to optimize the parameters of
interest. Realized in commodity time series data, the
proposed technique is compared against two comparable
techniques, including single GWO and LSSVM optimized
by Artificial Bee Colony (ABC) algorithm (ABC-LSSVM).
Empirical results suggested that the GWO-LSSVM is
capable to produce lower error rates as compared to the
identified algorithms for the price of interested time series
data. |
| first_indexed | 2025-11-15T01:45:48Z |
| format | Conference or Workshop Item |
| id | ump-11215 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:45:48Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-112152018-02-21T04:04:52Z http://umpir.ump.edu.my/id/eprint/11215/ LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting Zuriani, Mustaffa Mohd Herwan, Sulaiman M. N. M., Kahar QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering The importance of optimizing Least Squares Support Vector Machines (LSSVM) embedded control parameters has motivated researchers to search for proficient optimization techniques. In this study, a new metaheuristic algorithm, viz., Grey Wolf Optimizer (GWO), is employed to optimize the parameters of interest. Realized in commodity time series data, the proposed technique is compared against two comparable techniques, including single GWO and LSSVM optimized by Artificial Bee Colony (ABC) algorithm (ABC-LSSVM). Empirical results suggested that the GWO-LSSVM is capable to produce lower error rates as compared to the identified algorithms for the price of interested time series data. 2015 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/11215/1/LS-SVM%20Hyper-parameters%20Optimization%20based%20on%20GWO%20Algorithm%20for%20Time%20Series%20Forecasting.pdf Zuriani, Mustaffa and Mohd Herwan, Sulaiman and M. N. M., Kahar (2015) LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting. In: IEEE 4th International Conference On Software Engineering & Computer Systems (ICSECS15) , 19-21 August 2015 , Kuantan, Pahang. pp. 183-188.. (Published) http://dx.doi.org/10.1109/ICSECS.2015.7333107 |
| spellingShingle | QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering Zuriani, Mustaffa Mohd Herwan, Sulaiman M. N. M., Kahar LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting |
| title | LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting |
| title_full | LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting |
| title_fullStr | LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting |
| title_full_unstemmed | LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting |
| title_short | LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting |
| title_sort | ls-svm hyper-parameters optimization based on gwo algorithm for time series forecasting |
| topic | QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/11215/ http://umpir.ump.edu.my/id/eprint/11215/ http://umpir.ump.edu.my/id/eprint/11215/1/LS-SVM%20Hyper-parameters%20Optimization%20based%20on%20GWO%20Algorithm%20for%20Time%20Series%20Forecasting.pdf |