TIME SERIES MODELING AND FORECASTING OF DENGUE DEATH OCCURRENCE IN MALAYSIA USING SEASONAL ARIMA TECHNIQUES
The incidence of dengue cases and dengue death has grown-up dramatically around the world in recent decades. Recently, the number of reported cases, especially in Malaysia continued to increase. Over the year, many researchers try to estimate the number of deaths that cause by dengue. One of the methods in Biostatistics is ARIMA method which is involving time series analysis. Time series analysis commonly referred to any analysis which involved in a time series data. If the continuous observation is dependable, then the values that will come are able to be forecasted from the previous observation. The objective of this research paper is to forecast the number of dengue deaths, to describe the behavior of the time series data and afterwards made use of skilled statistical techniques for estimation, forecasting but also the controlling. In this paper the recognition of concerning the SARIMA (p,d,q) (P,D,Q)15 was given attention through the approach to the Autocorrelation Function ACF and Partial Autocorrelation Function (PACF) theory plot. SARIMA (2,1,0) (0,1,1)15 is being selected as the best model to represent the dengue death data. The gained model will be used as a tool for the prediction of the dengue death.
Keywords: Autoregressive Process model AR (p), Moving Average Process MA (q), SARIMA (p,d,q), time series, forecasting and dengue.