Mustaffa, Zuriani and Sulaiman, Mohd Herwan and Rohidin, Dede and Ernawan, Ferda and Kasim, Shahreen (2018) Time series predictive analysis based on hybridization of meta-heuristic algorithms. International Journal on Advanced Science Engineering Information Technology, 8 (5). pp. 1919-1925. ISSN 2088-5334
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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, CSLSSVM, 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 |
|---|---|
| Uncontrolled Keywords: | computational intelligence; least squares support vector machines; machine learning; meta-heuristic; optimization; swarm intelligence; time series prediction |
| Subjects: | Q Science > QA Mathematics > QA71-90 Instruments and machines |
| Divisions: | Faculty of Engineering Technology > Department of Electrical Engineering Technology |
| Depositing User: | UiTM Student Praktikal |
| Date Deposited: | 16 Nov 2021 08:05 |
| Last Modified: | 16 Nov 2021 08:05 |
| URI: | http://eprints.uthm.edu.my/id/eprint/3354 |
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