Forecasting Time Series Movement Direction with Hybrid Methodology

Salwa Waeto,Khanchit Chuarkham,Arthit Intarasit

doi:10.1155/2017/3174305

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.

Highlights

Time series modeling and forecasting are a challenge for describing dynamic phenomena and pattern behavior of the time series
An estimated model of (5) is formulated as follows: using linear statistic model to obtain an estimated model of linear component Lt denoted by Lt and after that modeling the residual yt−Lt which contains only the nonlinear relationship to obtain an estimated model of nonlinear component Nt denoted by Nt
Zhang [8] utilized the hybrid model by introducing the estimated model of (5) in the form ŷt = Lt + Nt, where Lt is prescribed by Autoregressive Integrated Moving Average (ARIMA) model and Nt is prescribed by feedforward neural networks model

Summary

Introduction

Time series modeling and forecasting are a challenge for describing dynamic phenomena and pattern behavior of the time series. According to the database of Deep South Watch [1], Jitpiromsri and Mccargo [2] and Jitpiromsri [3] reported the trends of Thailand’s south insurgency using diagram for comparing the monthly number of the unrest incidents. By applying a polynomial least-square regression, they provided the forecasting model for describing the unrest incidents in the south of Thailand. This polynomial is not fitting the monthly number of the unrest incidents as well. An autoregressive model of order p abbreviated as AR(p) model is p yt = φ1yt−1 + φ2yt−2 + ⋅ ⋅ ⋅ + φpyt−p + wt = ∑φiyt−i, (1)

Objectives

Methods

Conclusion