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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
%X 

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.

%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
AB 

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.

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