FORECASTING SAVING DEPOSIT IN MALAYSIAN ISLAMIC BANKING: COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORK AND ARIMA - Sukmana, Raditya; Solihin, Mahmud Iwan (Jurnal Ekonomi & Studi Pembangunan, 2007)
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 autoregresÂsive integrated moving average (ARIMA) in studying saving deposit in MalayÂsian Islamic banks. Artificial neural network is getting popular as an alternaÂtive method in time series forecasting for its capability to capture volaÂtility pattern of non-linear time series data. In addition, the use of an estabÂlished 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 bankÂing deposits from January 1994 to November 2005. The result provides evidence that ANN using â€œearly stoppingâ€ approach can be used as an alternaÂtive forecasting engine with univariate time series model. It can predict non-linÂear time series using the pattern of the data directly without any statistiÂcal analysis.