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

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:
QA76 Computer software
TK Electrical engineering. Electronics Nuclear engineering
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
id ump-11215
recordtype eprints
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.. http://dx.doi.org/10.1109/ICSECS.2015.7333107
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA76 Computer software
TK Electrical engineering. Electronics Nuclear engineering
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
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.
format Conference or Workshop Item
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
title 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_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_sort ls-svm hyper-parameters optimization based on gwo algorithm for time series forecasting
publishDate 2015
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
first_indexed 2023-09-18T22:11:42Z
last_indexed 2023-09-18T22:11:42Z
_version_ 1777415071012487168

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