FORECASTING SAVING DEPOSIT IN MALAYSIAN ISLAMIC BANKING: COMPARISON BETWEEN ARTIFICIAL NEURAL NETWORK AND ARIMA - Raditya Sukmana (Universitas Muhammadiyah Yogyakarta, 2014)
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.