A HYBRID LEAST SQUARES SUPPORT VECTOR MACHINE WITH BAT AND CUCKOO SEARCH ALGORITHMS FOR TIME SERIES FORECASTING

Least Squares Support Vector Machine (LSSVM) has been known to be one of the effective forecasting models. However, its operation relies on two important parameters (regularization and kernel). Pre-determining the values of parameters will affect the results of the forecasting model; hence, to find...

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Autores principales:	Athraa Jasim Mohammed, Khalil Ibrahim Ghathwan, Yuhanis Yusof
Formato:	article
Lenguaje:	EN
Publicado:	UUM Press 2020
Materias:	machine learning data mining time series forecasting least squares support vector machine particle swarm optimization Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/1d0782e9d6044450a648da9668b491a9
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spelling	oai:doaj.org-article:1d0782e9d6044450a648da9668b491a92021-11-15T04:05:06ZA HYBRID LEAST SQUARES SUPPORT VECTOR MACHINE WITH BAT AND CUCKOO SEARCH ALGORITHMS FOR TIME SERIES FORECASTING10.32890/jict2020.19.3.31675-414X2180-3862https://doaj.org/article/1d0782e9d6044450a648da9668b491a92020-06-01T00:00:00Zhttp://e-journal.uum.edu.my/index.php/jict/article/view/jict2020.19.3.3https://doaj.org/toc/1675-414Xhttps://doaj.org/toc/2180-3862Least Squares Support Vector Machine (LSSVM) has been known to be one of the effective forecasting models. However, its operation relies on two important parameters (regularization and kernel). Pre-determining the values of parameters will affect the results of the forecasting model; hence, to find the optimal value of these parameters, this study investigates the adaptation of Bat and Cuckoo Search algorithms to optimize LSSVM parameters. Even though Cuckoo Search has been proven to be able to solve global optimization in various areas, the algorithm leads to a slow convergence rate when the step size is large. Hence, to enhance the search ability of Cuckoo Search, it is integrated with Bat algorithm that offers a balanced search between global and local. Evaluation was performed separately to further analyze the strength of Bat and Cuckoo Search to optimize LSSVM parameters. Five evaluation metrics were utilized; mean average percent error (MAPE), accuracy, symmetric mean absolute percent error (SMAPE), root mean square percent error (RMSPE) and fitness value. Experimental results on diabetes forecasting demonstrated that the proposed BAT-LSSVM and CUCKOO-LSSVM generated lower MAPE and SMAPE, at the same time produced higher accuracy and fitness value compared to particle swarm optimization (PSO)-LSSVM and a non-optimized LSSVM. Following the success, this study has integrated the two algorithms to better optimize the LSSVM. The newly proposed forecasting algorithm, termed as CUCKOO-BAT-LSSVM, produces better forecasting in terms of MAPE, accuracy and RMSPE. Such an outcome provides an alternative model to be used in facilitating decision-making in forecasting. Athraa Jasim MohammedKhalil Ibrahim GhathwanYuhanis YusofUUM Pressarticlemachine learningdata miningtime series forecastingleast squares support vector machineparticle swarm optimizationInformation technologyT58.5-58.64ENJournal of ICT, Vol 19, Iss 3, Pp 351-379 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning data mining time series forecasting least squares support vector machine particle swarm optimization Information technology T58.5-58.64
spellingShingle	machine learning data mining time series forecasting least squares support vector machine particle swarm optimization Information technology T58.5-58.64 Athraa Jasim Mohammed Khalil Ibrahim Ghathwan Yuhanis Yusof A HYBRID LEAST SQUARES SUPPORT VECTOR MACHINE WITH BAT AND CUCKOO SEARCH ALGORITHMS FOR TIME SERIES FORECASTING
description	Least Squares Support Vector Machine (LSSVM) has been known to be one of the effective forecasting models. However, its operation relies on two important parameters (regularization and kernel). Pre-determining the values of parameters will affect the results of the forecasting model; hence, to find the optimal value of these parameters, this study investigates the adaptation of Bat and Cuckoo Search algorithms to optimize LSSVM parameters. Even though Cuckoo Search has been proven to be able to solve global optimization in various areas, the algorithm leads to a slow convergence rate when the step size is large. Hence, to enhance the search ability of Cuckoo Search, it is integrated with Bat algorithm that offers a balanced search between global and local. Evaluation was performed separately to further analyze the strength of Bat and Cuckoo Search to optimize LSSVM parameters. Five evaluation metrics were utilized; mean average percent error (MAPE), accuracy, symmetric mean absolute percent error (SMAPE), root mean square percent error (RMSPE) and fitness value. Experimental results on diabetes forecasting demonstrated that the proposed BAT-LSSVM and CUCKOO-LSSVM generated lower MAPE and SMAPE, at the same time produced higher accuracy and fitness value compared to particle swarm optimization (PSO)-LSSVM and a non-optimized LSSVM. Following the success, this study has integrated the two algorithms to better optimize the LSSVM. The newly proposed forecasting algorithm, termed as CUCKOO-BAT-LSSVM, produces better forecasting in terms of MAPE, accuracy and RMSPE. Such an outcome provides an alternative model to be used in facilitating decision-making in forecasting.
format	article
author	Athraa Jasim Mohammed Khalil Ibrahim Ghathwan Yuhanis Yusof
author_facet	Athraa Jasim Mohammed Khalil Ibrahim Ghathwan Yuhanis Yusof
author_sort	Athraa Jasim Mohammed
title	A HYBRID LEAST SQUARES SUPPORT VECTOR MACHINE WITH BAT AND CUCKOO SEARCH ALGORITHMS FOR TIME SERIES FORECASTING
title_short	A HYBRID LEAST SQUARES SUPPORT VECTOR MACHINE WITH BAT AND CUCKOO SEARCH ALGORITHMS FOR TIME SERIES FORECASTING
title_full	A HYBRID LEAST SQUARES SUPPORT VECTOR MACHINE WITH BAT AND CUCKOO SEARCH ALGORITHMS FOR TIME SERIES FORECASTING
title_fullStr	A HYBRID LEAST SQUARES SUPPORT VECTOR MACHINE WITH BAT AND CUCKOO SEARCH ALGORITHMS FOR TIME SERIES FORECASTING
title_full_unstemmed	A HYBRID LEAST SQUARES SUPPORT VECTOR MACHINE WITH BAT AND CUCKOO SEARCH ALGORITHMS FOR TIME SERIES FORECASTING
title_sort	hybrid least squares support vector machine with bat and cuckoo search algorithms for time series forecasting
publisher	UUM Press
publishDate	2020
url	https://doaj.org/article/1d0782e9d6044450a648da9668b491a9
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A HYBRID LEAST SQUARES SUPPORT VECTOR MACHINE WITH BAT AND CUCKOO SEARCH ALGORITHMS FOR TIME SERIES FORECASTING

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