Bootstrapping the Multilayer Feedforward Propagation System for Predicting the Arrival Guest in Malaysia

Abstract

Predicting arrival guest is an essential step in estimating the Malaysia economic impact, particularly in short and long-term COVID-19 crisis. Neural Network family of models has been widely used in economic and tourism. Most of the study used single layer of fed forward propagation system, it is because of less sampling variation in Neural Network. However, apparently the multilayer provides a better predicting result but its disadvantage is to deal with high sampling variation. The motivation of this study is to enhance the ability of multilayer Neural Network in predicting the arrival guest. In this study, a hybrid model based on Bootstrapping the Neural Network proposed to predict the arrival guest of Singapore in Malaysia. The weights of variables to the first hidden layer nodes are bootstrapped. The subsequent hidden layer takes the bootstrapped weights in order to obtain the Neural Network output. The prediction obtained by hybrid model has been compared with conventional multilayer Neural Network in terms of small variation. The computational results shows that the hybrid model provides better performance in predicting the arrival guest.