Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction

Over the past decades, the Least Squares Support Vector Machines (LSSVM) has been widely utilized in prediction task of various application domains. Nevertheless, existing literature showed that the capability of LSSVM is highly dependent on the value of its hyper-parameters, namely regularization p...

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Main Author:	Zuriani, Mustaffa
Format:	Thesis
Language:	eng eng
Published:	2014
Subjects:	QA76 Computer software
Online Access:	https://etd.uum.edu.my/4394/1/s93651.pdf https://etd.uum.edu.my/4394/2/s93651_abstract.pdf
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id	my-uum-etd.4394
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Yusof, Yuhanis Kamaruddin, Siti Sakira
topic	QA76 Computer software
spellingShingle	QA76 Computer software Zuriani, Mustaffa Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction
description	Over the past decades, the Least Squares Support Vector Machines (LSSVM) has been widely utilized in prediction task of various application domains. Nevertheless, existing literature showed that the capability of LSSVM is highly dependent on the value of its hyper-parameters, namely regularization parameter and kernel parameter, where this would greatly affect the generalization of LSSVM in prediction task. This study proposed a hybrid algorithm, based on Artificial Bee Colony (ABC) and LSSVM, that consists of three algorithms; ABC-LSSVM, lvABC-LSSVM and cmABC-LSSVM. The lvABC algorithm is introduced to overcome the local optima problem by enriching the searching behaviour using Levy mutation. On the other hand, the cmABC algorithm that incorporates conventional mutation addresses the over- fitting or under-fitting problem. The combination of lvABC and cmABC algorithm, which is later introduced as Enhanced Artificial Bee Colony–Least Squares Support Vector Machine (eABC-LSSVM), is realized in prediction of non renewable natural resources commodity price. Upon the completion of data collection and data pre processing, the eABC-LSSVM algorithm is designed and developed. The predictability of eABC-LSSVM is measured based on five statistical metrics which include Mean Absolute Percentage Error (MAPE), prediction accuracy, symmetric MAPE (sMAPE), Root Mean Square Percentage Error (RMSPE) and Theils’ U. Results showed that the eABC-LSSVM possess lower prediction error rate as compared to eight hybridization models of LSSVM and Evolutionary Computation (EC) algorithms. In addition, the proposed algorithm is compared to single prediction techniques, namely, Support Vector Machines (SVM) and Back Propagation Neural Network (BPNN). In general, the eABC-LSSVM produced more than 90% prediction accuracy. This indicates that the proposed eABC-LSSVM is capable of solving optimization problem, specifically in the prediction task. The eABC-LSSVM is hoped to be useful to investors and commodities traders in planning their investment and projecting their profit.
format	Thesis
qualification_name	Ph.D.
qualification_level	Doctorate
author	Zuriani, Mustaffa
author_facet	Zuriani, Mustaffa
author_sort	Zuriani, Mustaffa
title	Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction
title_short	Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction
title_full	Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction
title_fullStr	Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction
title_full_unstemmed	Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction
title_sort	enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2014
url	https://etd.uum.edu.my/4394/1/s93651.pdf https://etd.uum.edu.my/4394/2/s93651_abstract.pdf
_version_	1747827729916493824
spelling	my-uum-etd.43942022-06-09T01:00:24Z Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction 2014 Zuriani, Mustaffa Yusof, Yuhanis Kamaruddin, Siti Sakira Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA76 Computer software Over the past decades, the Least Squares Support Vector Machines (LSSVM) has been widely utilized in prediction task of various application domains. Nevertheless, existing literature showed that the capability of LSSVM is highly dependent on the value of its hyper-parameters, namely regularization parameter and kernel parameter, where this would greatly affect the generalization of LSSVM in prediction task. This study proposed a hybrid algorithm, based on Artificial Bee Colony (ABC) and LSSVM, that consists of three algorithms; ABC-LSSVM, lvABC-LSSVM and cmABC-LSSVM. The lvABC algorithm is introduced to overcome the local optima problem by enriching the searching behaviour using Levy mutation. On the other hand, the cmABC algorithm that incorporates conventional mutation addresses the over- fitting or under-fitting problem. The combination of lvABC and cmABC algorithm, which is later introduced as Enhanced Artificial Bee Colony–Least Squares Support Vector Machine (eABC-LSSVM), is realized in prediction of non renewable natural resources commodity price. Upon the completion of data collection and data pre processing, the eABC-LSSVM algorithm is designed and developed. The predictability of eABC-LSSVM is measured based on five statistical metrics which include Mean Absolute Percentage Error (MAPE), prediction accuracy, symmetric MAPE (sMAPE), Root Mean Square Percentage Error (RMSPE) and Theils’ U. Results showed that the eABC-LSSVM possess lower prediction error rate as compared to eight hybridization models of LSSVM and Evolutionary Computation (EC) algorithms. In addition, the proposed algorithm is compared to single prediction techniques, namely, Support Vector Machines (SVM) and Back Propagation Neural Network (BPNN). In general, the eABC-LSSVM produced more than 90% prediction accuracy. This indicates that the proposed eABC-LSSVM is capable of solving optimization problem, specifically in the prediction task. The eABC-LSSVM is hoped to be useful to investors and commodities traders in planning their investment and projecting their profit. 2014 Thesis https://etd.uum.edu.my/4394/ https://etd.uum.edu.my/4394/1/s93651.pdf text eng public https://etd.uum.edu.my/4394/2/s93651_abstract.pdf text eng public Ph.D. doctoral Universiti Utara Malaysia Abdullah, S. N., & Zeng, X. (2010, July 18-23). 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