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Time Series Predictive Analysis based on Hybridization of Meta-heuristic Algorithms
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@article{IJASEIT4968,
author = {Zuriani Mustaffa and Mohd Herwan Sulaiman and Dede Rohidin and Ferda Ernawan and Shahreen Kasim},
title = {Time Series Predictive Analysis based on Hybridization of Meta-heuristic Algorithms},
journal = {International Journal on Advanced Science, Engineering and Information Technology},
volume = {8},
number = {5},
year = {2018},
pages = {1919--1925},
keywords = {computational intelligence; least squares support vector machines; machine learning; meta-heuristic; optimization; swarm intelligence; time series prediction},
abstract = {This paper presents a comparative study which involved five hybrid meta-heuristic methods to predict the weather five days in advance. The identified meta-heuristic methods namely Moth-flame Optimization (MFO), Cuckoo Search algorithm (CSA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Differential Evolution (DE) are individually hybridized with a well-known machine learning technique namely Least Squares Support Vector Machines (LS-SVM). For experimental purposes, a total of 6 independent inputs are considered which were collected based on daily weather data. The efficiency of the MFO-LSSVM, CS-LSSVM, ABC-LSSVM, FA-LSSVM, and DE-LSSVM was quantitatively analyzed based on Theil’s U and Root Mean Square Percentage Error. Overall, the experimental results demonstrate a good rival among the identified methods. However, the superiority goes to FA-LSSVM which was able to record lower error rates in prediction. The proposed prediction model could benefit many parties in continuity planning daily activities.
},
issn = {2088-5334},
publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4968},
doi = {10.18517/ijaseit.8.5.4968}
}
EndNote
%A Mustaffa, Zuriani %A Sulaiman, Mohd Herwan %A Rohidin, Dede %A Ernawan, Ferda %A Kasim, Shahreen %D 2018 %T Time Series Predictive Analysis based on Hybridization of Meta-heuristic Algorithms %B 2018 %9 computational intelligence; least squares support vector machines; machine learning; meta-heuristic; optimization; swarm intelligence; time series prediction %! Time Series Predictive Analysis based on Hybridization of Meta-heuristic Algorithms %K computational intelligence; least squares support vector machines; machine learning; meta-heuristic; optimization; swarm intelligence; time series prediction %XThis paper presents a comparative study which involved five hybrid meta-heuristic methods to predict the weather five days in advance. The identified meta-heuristic methods namely Moth-flame Optimization (MFO), Cuckoo Search algorithm (CSA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Differential Evolution (DE) are individually hybridized with a well-known machine learning technique namely Least Squares Support Vector Machines (LS-SVM). For experimental purposes, a total of 6 independent inputs are considered which were collected based on daily weather data. The efficiency of the MFO-LSSVM, CS-LSSVM, ABC-LSSVM, FA-LSSVM, and DE-LSSVM was quantitatively analyzed based on Theil’s U and Root Mean Square Percentage Error. Overall, the experimental results demonstrate a good rival among the identified methods. However, the superiority goes to FA-LSSVM which was able to record lower error rates in prediction. The proposed prediction model could benefit many parties in continuity planning daily activities.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4968 %R doi:10.18517/ijaseit.8.5.4968 %J International Journal on Advanced Science, Engineering and Information Technology %V 8 %N 5 %@ 2088-5334
IEEE
Zuriani Mustaffa,Mohd Herwan Sulaiman,Dede Rohidin,Ferda Ernawan and Shahreen Kasim,"Time Series Predictive Analysis based on Hybridization of Meta-heuristic Algorithms," International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 5, pp. 1919-1925, 2018. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.8.5.4968.
RefMan/ProCite (RIS)
TY - JOUR AU - Mustaffa, Zuriani AU - Sulaiman, Mohd Herwan AU - Rohidin, Dede AU - Ernawan, Ferda AU - Kasim, Shahreen PY - 2018 TI - Time Series Predictive Analysis based on Hybridization of Meta-heuristic Algorithms JF - International Journal on Advanced Science, Engineering and Information Technology; Vol. 8 (2018) No. 5 Y2 - 2018 SP - 1919 EP - 1925 SN - 2088-5334 PB - INSIGHT - Indonesian Society for Knowledge and Human Development KW - computational intelligence; least squares support vector machines; machine learning; meta-heuristic; optimization; swarm intelligence; time series prediction N2 -This paper presents a comparative study which involved five hybrid meta-heuristic methods to predict the weather five days in advance. The identified meta-heuristic methods namely Moth-flame Optimization (MFO), Cuckoo Search algorithm (CSA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Differential Evolution (DE) are individually hybridized with a well-known machine learning technique namely Least Squares Support Vector Machines (LS-SVM). For experimental purposes, a total of 6 independent inputs are considered which were collected based on daily weather data. The efficiency of the MFO-LSSVM, CS-LSSVM, ABC-LSSVM, FA-LSSVM, and DE-LSSVM was quantitatively analyzed based on Theil’s U and Root Mean Square Percentage Error. Overall, the experimental results demonstrate a good rival among the identified methods. However, the superiority goes to FA-LSSVM which was able to record lower error rates in prediction. The proposed prediction model could benefit many parties in continuity planning daily activities.
UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4968 DO - 10.18517/ijaseit.8.5.4968
RefWorks
RT Journal Article ID 4968 A1 Mustaffa, Zuriani A1 Sulaiman, Mohd Herwan A1 Rohidin, Dede A1 Ernawan, Ferda A1 Kasim, Shahreen T1 Time Series Predictive Analysis based on Hybridization of Meta-heuristic Algorithms JF International Journal on Advanced Science, Engineering and Information Technology VO 8 IS 5 YR 2018 SP 1919 OP 1925 SN 2088-5334 PB INSIGHT - Indonesian Society for Knowledge and Human Development K1 computational intelligence; least squares support vector machines; machine learning; meta-heuristic; optimization; swarm intelligence; time series prediction ABThis paper presents a comparative study which involved five hybrid meta-heuristic methods to predict the weather five days in advance. The identified meta-heuristic methods namely Moth-flame Optimization (MFO), Cuckoo Search algorithm (CSA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Differential Evolution (DE) are individually hybridized with a well-known machine learning technique namely Least Squares Support Vector Machines (LS-SVM). For experimental purposes, a total of 6 independent inputs are considered which were collected based on daily weather data. The efficiency of the MFO-LSSVM, CS-LSSVM, ABC-LSSVM, FA-LSSVM, and DE-LSSVM was quantitatively analyzed based on Theil’s U and Root Mean Square Percentage Error. Overall, the experimental results demonstrate a good rival among the identified methods. However, the superiority goes to FA-LSSVM which was able to record lower error rates in prediction. The proposed prediction model could benefit many parties in continuity planning daily activities.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4968 DO - 10.18517/ijaseit.8.5.4968
