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...

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Main Authors: Zuriani, Mustaffa, Mohd Herwan, Sulaiman, M. N. M., Kahar
Format: Conference or Workshop Item
Language:English
Published: 2015
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/11215/1/LS-SVM%20Hyper-parameters%20Optimization%20based%20on%20GWO%20Algorithm%20for%20Time%20Series%20Forecasting.pdf
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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
collection UMP
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.
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spelling UMPir112152018-02-21T04:04:52Z 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/11215/1/LS-SVM%20Hyper-parameters%20Optimization%20based%20on%20GWO%20Algorithm%20for%20Time%20Series%20Forecasting.pdf
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AT mohdherwansulaiman lssvmhyperparametersoptimizationbasedongwoalgorithmfortimeseriesforecasting
AT mnmkahar lssvmhyperparametersoptimizationbasedongwoalgorithmfortimeseriesforecasting