Comparing of ARIMA and RBFNN for short-term forecasting
Abstract
Based on a combination of an autoregressive integrated moving
average (ARIMA) and a radial basis function neural network
(RBFNN), a time-series forecasting model is proposed. The proposed
model has examined using simulated time series data of tourist arrival
to Indonesia recently published by BPS Indonesia. The results
demonstrate that the proposed RBFNN is more competent in
modelling and forecasting time series than an ARIMA model which is
indicated by mean square error (MSE) values. Based on the results
obtained, RBFNN model is recommended as an alternative to existing
method because it has a simple structure and can produce reasonable
forecasts.