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

Zuriani Mustaffa, Mohd Herwan Sulaiman, Dede Rohidin, Ferda Ernawan, Shahreen Kasim


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.


computational intelligence; least squares support vector machines; machine learning; meta-heuristic; optimization; swarm intelligence; time series prediction

Full Text:


DOI: http://dx.doi.org/10.18517/ijaseit.8.5.4968


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development