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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ump-109072018-02-26T08:03:35Z 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.. http://dx.doi.org/10.1109/ICIEV.2015.7334054 |
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QA75 Electronic computers. Computer science Zuriani, Mustaffa M. H., Sulaiman M. N. M., Kahar Training LSSVM with GWO for Price Forecasting |
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. |
format |
Conference or Workshop Item |
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 |
title |
Training LSSVM with GWO for Price Forecasting |
title_short |
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_sort |
training lssvm with gwo for price forecasting |
publishDate |
2015 |
url |
http://umpir.ump.edu.my/id/eprint/10907/ http://umpir.ump.edu.my/id/eprint/10907/ 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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2023-09-18T22:11:03Z |
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2023-09-18T22:11:03Z |
_version_ |
1777415030155771904 |