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: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2015
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| Subjects: | |
| Online Access: | 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 |
| Summary: | 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. |
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