A Hybrid Neural Network Model to Forecast Arrival Guest in Malaysia

Abstract

Improving the forecasting estimation is significantly contributes to the growth of time series estimation. In this paper, based on the set of integrating data from autoregressive integrated moving average (SARIMA) model, we hybrid it in artificial neural network (ANN) algorithm to quantify nonlinearity part of SARIMA model and improve the forecasting estimation. This hybrid methodology is apply to Malaysia arrival guest historical data. The forecasting performance of the hybrid approach is compared to individual model of SARIMA and ANN. We found that the hybrid approach results are remarkably improved the correlation and error estimation. Thus, this improvement shows that the forecasting is improved with the hybrid SARIMA-ANN model.