Time series predictive analysis based on hybridization of meta-heuristic algorithms

Zuriani, Mustaffa and M. H., Sulaiman and Rohidin, Dede and Ernawan, Ferda and Shahreen, Kasim (2018) Time series predictive analysis based on hybridization of meta-heuristic algorithms. International Journal on Advanced Science, Engineering and Information Technology, 8 (5). pp. 1919-1925. ISSN 2088-5334. (Published)

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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.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Computational intelligence; Least squares support vector machines; Machine learning; Meta-heuristic; Optimization; Swarm intelligence; Time series prediction
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Faculty of Electrical & Electronic Engineering
Depositing User: Dr. Ferda Ernawan
Date Deposited: 25 Oct 2021 04:17
Last Modified: 25 Oct 2021 04:17
URI: http://umpir.ump.edu.my/id/eprint/30076
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