Time Series Predictive Analysis based on Hybridization of Meta-heuristic Algorithms

Zuriani Mustaffa (1), Mohd Herwan Sulaiman (2), Dede Rohidin (3), Ferda Ernawan (4), Shahreen Kasim (5)
(1) Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, 26300 Gambang, Pahang, Malaysia
(2) Faculty of Electrical & Electronics Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia.
(3) School of Computing, Telkom University, 40257 Bandung, West Java, Indonesia
(4) Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, 26300 Gambang, Pahang, Malaysia
(5) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, 86400, Malaysia
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How to cite (IJASEIT) :
Mustaffa, Zuriani, et al. “Time Series Predictive Analysis Based on Hybridization of Meta-Heuristic Algorithms”. International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 5, Oct. 2018, pp. 1919-25, doi:10.18517/ijaseit.8.5.4968.
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.
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