Forecasting Time Series Movement Direction with Hybrid Methodology

Salwa Waeto, Khanchit Chuarkham and Arthit Intarasit

Journal of Probability and Statistics, 2017, vol. 2017, 1-8

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnljps:3174305

DOI: 10.1155/2017/3174305

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