FORECASTING THE NUMBER OF AIRPLANE PASSENGERS USING BOX-JENKINS AND ARTIFICIAL NEURAL NETWORK IN MALAYSIA

Abstract

Airline industry is one of the largest industries in the world of transport because it is the most important transport in the global transport system. The airline industry has played a very important role in the economic development in Malaysia. Due to the increase in its operating business, the demand for air travel increases day by day. Hence, this study focused on the number of passengers using air transport in Malaysia. The monthly data from January 2005 to December 2015 were obtained from Malaysia Airport Holdings Berhad (MAHB) in Sepang, Selangor. The data is divided into 2 parts, which are in sample data from January 2005 to December 2014 and out sample data from January 2015 to December 2015. The study was conducted to predict airline passengers in Malaysia using the Box-Jenkins model and Artificial Neural Network (ANN) model. Both models were studied to choose the best model. Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) were used to measure the performance of both models. SARIMA was selected as the best model for Box-Jenkins with MAPE and MSE were 7.3458388 and 2.67011 respectively while Multilayer Feed Forward Neural Network (MFFNN) with seven input variables, with MAPE and MSE, 7.251 and 0.0006 respectively were selected as the best model for Multilayer Feed Forward Neural Network (FFNN). In conclusion, these studies have proven that the Multilayer Feed Forward Neural Network (FFNN) model is the best model for considering airplanes in Malaysia compared to the SARIMA model.