Forecasting Dengue Haemorrhagic Fever Cases in Southern Thailand using ARIMA Models (original) (raw)

Abstract

A univariate time-series analysis method has been used to model and forecast the monthly number of dengue haemorrhagic fever (DHF) cases in southern Thailand. We developed autoregressive integrated moving average (ARIMA) models on the data collected between 1994-2005 and then validated the models using the data collected between January-August 2006. The results showed that the regressive forecast curves were consistent

Figures (6)

autoregressive integrated moving average (ARIMA) models. The ARIMA models were analysed with the Box-Jenkins approach, which was appropriate for a long forecasting period.'""! This method for selecting an appropriate ARIMA model for estimating and forecasting a univariate time-series consisted of identification, estimation, diagnostic checking and forecasting." First, a check for stationary was made with the aid of a control chart, which was a useful graphical device for detecting the lag of stationary in a time-series analysis. With 144 monthly values used for model synthesis, only correlations at the first 144/4 = 36 lags needed to be examined."”! The basic idea was

autoregressive integrated moving average (ARIMA) models. The ARIMA models were analysed with the Box-Jenkins approach, which was appropriate for a long forecasting period.'""! This method for selecting an appropriate ARIMA model for estimating and forecasting a univariate time-series consisted of identification, estimation, diagnostic checking and forecasting." First, a check for stationary was made with the aid of a control chart, which was a useful graphical device for detecting the lag of stationary in a time-series analysis. With 144 monthly values used for model synthesis, only correlations at the first 144/4 = 36 lags needed to be examined."”! The basic idea was

Figure 2(a): Autocorrelation function of DHF time-series Figure 2(b): Partial autocorrelation function of DHF time-series

Figure 2(a): Autocorrelation function of DHF time-series Figure 2(b): Partial autocorrelation function of DHF time-series

Figure 3: Scatter plot of the residuals of the ARIMA (1,0,1) model

Figure 3: Scatter plot of the residuals of the ARIMA (1,0,1) model

Figure 4: Autocorrelation function of the residuals of ARIMA (1,0,1) model

Figure 4: Autocorrelation function of the residuals of ARIMA (1,0,1) model

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