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Journal of Probability and Statistics
Volume 2017, Article ID 3174305, 8 pages
https://doi.org/10.1155/2017/3174305
Research Article

Forecasting Time Series Movement Direction with Hybrid Methodology

1Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Pattani 94000, Thailand
2Centre of Excellence in Mathematics, Commission on Higher Education, Ratchathewi, Bangkok 10400, Thailand
3Faculty of Commerce and Management, Prince of Songkla University, Trang Campus, Trang 92000, Thailand

Correspondence should be addressed to Arthit Intarasit; moc.liamg@tisaratni.a

Received 30 November 2016; Revised 26 March 2017; Accepted 4 April 2017; Published 31 July 2017

Academic Editor: Dejian Lai

Copyright © 2017 Salwa Waeto et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Forecasting the tendencies of time series is a challenging task which gives better understanding. The purpose of this paper is to present the hybrid model of support vector regression associated with Autoregressive Integrated Moving Average which is formulated by hybrid methodology. The proposed model is more convenient for practical usage. The tendencies modeling of time series for Thailand’s south insurgency is of interest in this research article. The empirical results using the time series of monthly number of deaths, injuries, and incidents for Thailand’s south insurgency indicate that the proposed hybrid model is an effective way to construct an estimated hybrid model which is better than the classical time series model or support vector regression. The best forecast accuracy is performed by using mean square error.