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Gasoline price forecasting: An application of LSSVM with improved ABC

Optimizing the hyper-parameters of Least Squares Support Vector Machines (LSSVM) is crucial as it will directly influence the predictive power of the algorithm.To tackle such issue, this study proposes an improved Artificial Bee Colony (IABC) algorithm which is based on conventional mutation.The IAB...

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Main Authors: Mustaffa, Zuriani, Yusof, Yuhanis, Kamaruddin, Siti Sakira
格式: Conference or Workshop Item
語言:English
出版: Elsevier Ltd. 2014
主題:
QA76 Computer software
在線閱讀:http://repo.uum.edu.my/14831/1/1-s2.0RG.pdf
http://repo.uum.edu.my/14831/
http://doi.org/10.1016/j.sbspro.2014.03.718
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spelling my.uum.repo.148312016-05-23T07:26:52Z http://repo.uum.edu.my/14831/ Gasoline price forecasting: An application of LSSVM with improved ABC Mustaffa, Zuriani Yusof, Yuhanis Kamaruddin, Siti Sakira QA76 Computer software Optimizing the hyper-parameters of Least Squares Support Vector Machines (LSSVM) is crucial as it will directly influence the predictive power of the algorithm.To tackle such issue, this study proposes an improved Artificial Bee Colony (IABC) algorithm which is based on conventional mutation.The IABC serves as an optimizer for LSSVM.Realized in gasoline price forecasting, the performance is guided based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE).The conducted simulation results show that, the proposed IABCLSSVM outperforms the results produced by ABC-LSSVM and also the Back Propagation Neural Network. Elsevier Ltd. 2014 Conference or Workshop Item PeerReviewed application/pdf en cc_by http://repo.uum.edu.my/14831/1/1-s2.0RG.pdf Mustaffa, Zuriani and Yusof, Yuhanis and Kamaruddin, Siti Sakira (2014) Gasoline price forecasting: An application of LSSVM with improved ABC. In: 2nd International Conference on Innovation, Management and Technology Research, 22 – 23 September, 2013, Klana Resort, Negeri Sembilan, Malaysia. http://doi.org/10.1016/j.sbspro.2014.03.718 doi:10.1016/j.sbspro.2014.03.718
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Mustaffa, Zuriani
Yusof, Yuhanis
Kamaruddin, Siti Sakira
Gasoline price forecasting: An application of LSSVM with improved ABC
description Optimizing the hyper-parameters of Least Squares Support Vector Machines (LSSVM) is crucial as it will directly influence the predictive power of the algorithm.To tackle such issue, this study proposes an improved Artificial Bee Colony (IABC) algorithm which is based on conventional mutation.The IABC serves as an optimizer for LSSVM.Realized in gasoline price forecasting, the performance is guided based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE).The conducted simulation results show that, the proposed IABCLSSVM outperforms the results produced by ABC-LSSVM and also the Back Propagation Neural Network.
format Conference or Workshop Item
author Mustaffa, Zuriani
Yusof, Yuhanis
Kamaruddin, Siti Sakira
author_facet Mustaffa, Zuriani
Yusof, Yuhanis
Kamaruddin, Siti Sakira
author_sort Mustaffa, Zuriani
title Gasoline price forecasting: An application of LSSVM with improved ABC
title_short Gasoline price forecasting: An application of LSSVM with improved ABC
title_full Gasoline price forecasting: An application of LSSVM with improved ABC
title_fullStr Gasoline price forecasting: An application of LSSVM with improved ABC
title_full_unstemmed Gasoline price forecasting: An application of LSSVM with improved ABC
title_sort gasoline price forecasting: an application of lssvm with improved abc
publisher Elsevier Ltd.
publishDate 2014
url http://repo.uum.edu.my/14831/1/1-s2.0RG.pdf
http://repo.uum.edu.my/14831/
http://doi.org/10.1016/j.sbspro.2014.03.718
_version_ 1644281557728362496
score 13.149126

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