LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting

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..

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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.

Item Type: Conference or Workshop Item (Speech)
Additional Information: ISBN: 978-1-4673-6722-6
Uncontrolled Keywords: Grey Wolf Optimizer, Least Squares Support Vector Machines, Time Series Forecasting
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Computer System And Software Engineering
Faculty of Electrical & Electronic Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 20 Nov 2015 03:13
Last Modified: 21 Feb 2018 04:04
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