Warning: mysql_result() [function.mysql-result]: Unable to jump to row 0 on MySQL result index 129 in /home/insiyorg/public_html/ijaseit/plugins/system/jumi.php(63) : eval()'d code on line 22

Warning: mysql_result() [function.mysql-result]: Unable to jump to row 0 on MySQL result index 129 in /home/insiyorg/public_html/ijaseit/plugins/system/jumi.php(63) : eval()'d code on line 23

Warning: mysql_result() [function.mysql-result]: Unable to jump to row 0 on MySQL result index 129 in /home/insiyorg/public_html/ijaseit/plugins/system/jumi.php(63) : eval()'d code on line 24

Warning: mysql_result() [function.mysql-result]: Unable to jump to row 0 on MySQL result index 129 in /home/insiyorg/public_html/ijaseit/plugins/system/jumi.php(63) : eval()'d code on line 25
International Journal on Advanced Science, Engineering and Information Technology, Vol. () No. , DOI:10.18517/ijaseit...4968

Hybridization of Meta-heuristic Algorithms for Time Series Predictive Analysis

Zuriani Mustaffa, Mohd Herwan Sulaiman, Ferda Ernawan

Abstract

This paper attempts to provide 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 (CS), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Differential Evolution (DE) are individually hybridized with a well-known machine learning algorithm viz. Least Squares Support Vector Machines (LSSVM). For experimental purposes, a total of 6 influential variables are considered which were based on daily weather data. The efficiency of the MFO-LSSVM, CS-LSSVM, ABC-LSSVM, FA-LSSVM and DE-LSSVM were quantitatively analyzed based on Root Mean Square Percentage Error and Theil’s U. 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 may parties in continuity planning daily activities

Keywords:

Computational Intelligence; Least Squares Support Vector Machines; Machine learning; Meta-heuristic; Optimization; Swarm Intelligence; Time series prediction

Viewed: 18 times (since Sept 4, 2017)

cite this paper