A comparison between single exponential smoothing (SES), double exponential smoothing (DES), holts (brown) and adaptive response rate exponential smoothing (ARRES) techniques in forecasting Malaysia population

This research develops techniques which are helpful in forecasting univariate time series data. The techniques used in this study are Single Exponential Smoothing (SES), Double Exponential Smoothing (DES), Holts (Brown) and Adaptive Response Rate Exponential Smoothing (ARRES) Techniques. For the purpose of this study, secondary data of Malaysia Population covering the period 1957 up to 2013 was obtained from the Department of Statistics Malaysia. From the result obtained, Holts method was found to be the best method to forecast the Malaysia population since it produces the lowest Mean Square Error (MSE) value which is 38,273.3 compared to 210,480.29 for SES, 38,827.7 for DEB and 209,835.8 for ARRES techniques. Keywords: Univariate, Forecasting, Single Exponential Smoothing, Double Exponential Smoothing, And Adaptive Response Rate Exponential Smoothing, Holts (Brown).