International Journal of Advances in Intelligent Informatics
Vol 1, No 1 (2015): March 2015

Comparing of ARIMA and RBFNN for short-term forecasting

Haviluddin Haviluddin (Universitas Mulawarman)
Ahmad Jawahir (Researcher at ICT of Mulawarman University)

Article Info

Publish Date
31 Mar 2015


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.

Copyrights © 2015

Journal Info





Computer Science & IT


International journal of advances in intelligent informatics (IJAIN) e-ISSN: 2442-6571 is a peer reviewed open-access journal published three times a year in English-language, provides scientists and engineers throughout the world for the exchange and dissemination of theoretical and ...