FORECASTING SAVING DEPOSIT IN MALAYSIAN ISLAMIC BANKING: COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORK AND ARIMA

The aim of this paper is to test the ability of artificial neural network (ANN) as an alternative method in time series forecasting and compared to autoregres­sive integrated moving average (ARIMA) in studying saving deposit in Malay­sian Islamic banks. Artificial neural network is getting popular as an alterna­tive method in time series forecasting for its capability to capture vola­tility pattern of non-linear time series data. In addition, the use of an estab­lished tool of analysis such as ARIMA is of importance here for comparative purposes. These two methods are applied to monthly data of the Malaysian Islamic bank­ing deposits from January 1994 to November 2005. The result provides evidence that ANN using “early stopping” approach can be used as an alterna­tive forecasting engine with univariate time series model. It can predict non-lin­ear time series using the pattern of the data directly without any statisti­cal analysis