Training LSSVM with GWO for Price Forecasting

This paper presents a hybrid forecasting model namely Grey Wolf Optimizer-Least Squares Support Vector Machines (GWO-LSSVM). In this study, a great deal of attention was paid in determining LSSVM’s hyper parameters. For that matter, the GWO is utilized an optimization tool for optimizing the said...

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Main Authors: Zuriani, Mustaffa, M. H., Sulaiman, M. N. M., Kahar
Format: Conference or Workshop Item
Language:English
English
Published: 2015
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/10907/1/Training%20LSSVM%20with%20GWO%20for%20Price%20Forecasting.pdf
http://umpir.ump.edu.my/id/eprint/10907/7/fskkp-zuriani%20mustaffa-training%20lssvm.pdf
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author Zuriani, Mustaffa
M. H., Sulaiman
M. N. M., Kahar
author_facet Zuriani, Mustaffa
M. H., Sulaiman
M. N. M., Kahar
author_sort Zuriani, Mustaffa
collection UMP
description This paper presents a hybrid forecasting model namely Grey Wolf Optimizer-Least Squares Support Vector Machines (GWO-LSSVM). In this study, a great deal of attention was paid in determining LSSVM’s hyper parameters. For that matter, the GWO is utilized an optimization tool for optimizing the said hyper parameters. Realized in gold price forecasting, the feasibility of GWO-LSSVM is measured based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE). Upon completing the simulation tasks, the comparison against two hybrid methods suggested that the GWO-LSSVM capable to produce lower forecasting error as compared to the identified forecasting techniques.
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spelling UMPir109072018-02-26T08:03:35Z http://umpir.ump.edu.my/id/eprint/10907/ Training LSSVM with GWO for Price Forecasting Zuriani, Mustaffa M. H., Sulaiman M. N. M., Kahar QA75 Electronic computers. Computer science This paper presents a hybrid forecasting model namely Grey Wolf Optimizer-Least Squares Support Vector Machines (GWO-LSSVM). In this study, a great deal of attention was paid in determining LSSVM’s hyper parameters. For that matter, the GWO is utilized an optimization tool for optimizing the said hyper parameters. Realized in gold price forecasting, the feasibility of GWO-LSSVM is measured based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE). Upon completing the simulation tasks, the comparison against two hybrid methods suggested that the GWO-LSSVM capable to produce lower forecasting error as compared to the identified forecasting techniques. 2015 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/10907/1/Training%20LSSVM%20with%20GWO%20for%20Price%20Forecasting.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/10907/7/fskkp-zuriani%20mustaffa-training%20lssvm.pdf Zuriani, Mustaffa and M. H., Sulaiman and M. N. M., Kahar (2015) Training LSSVM with GWO for Price Forecasting. In: 4th International Conference on Informatics, Electronics and Vision (ICIEV2015) , 15-18 Jun 2015 , Fukuoka, Japan. pp. 1-6.. (Published) http://dx.doi.org/10.1109/ICIEV.2015.7334054
spellingShingle QA75 Electronic computers. Computer science
Zuriani, Mustaffa
M. H., Sulaiman
M. N. M., Kahar
Training LSSVM with GWO for Price Forecasting
title Training LSSVM with GWO for Price Forecasting
title_full Training LSSVM with GWO for Price Forecasting
title_fullStr Training LSSVM with GWO for Price Forecasting
title_full_unstemmed Training LSSVM with GWO for Price Forecasting
title_short Training LSSVM with GWO for Price Forecasting
title_sort training lssvm with gwo for price forecasting
topic QA75 Electronic computers. Computer science
url http://umpir.ump.edu.my/id/eprint/10907/1/Training%20LSSVM%20with%20GWO%20for%20Price%20Forecasting.pdf
http://umpir.ump.edu.my/id/eprint/10907/7/fskkp-zuriani%20mustaffa-training%20lssvm.pdf
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