A comparison of traditional and neural networks forecasting techniques for container throughput at bangkok port

Abstract

Containerization is one of the important factors for Thailand's economics. However, forecasts of container throughput growth and development of Bangkok Port, the significant port of Thailand, have been scant and the findings are divergence. Moreover, the existing literature emphasizes only two forecasting methods, namely time series and regression analysis. The aim of this paper is to explore Multilayer Perceptron (MLP) and Linear Regression for predicting future container throughput at Bangkok Port. Factors affecting cargo throughput at Bangkok Port were identified and then collected from Bank of Thailand, Office of the National Economic and Social Development Board, World Bank, Ministry of Interior, and Energy Policy and Planning Office. These factors were entered into MLP and Linear Regression forecasting models that generated a projection of cargo throughput. Subsequently, the results were measured by root mean squared error (RMSE) and mean absolute error (MAE). Based on the results, this research provides the best application of forecasting technique which is Neural Network - Multilayer Perceptron technique for predicting container throughput at Bangkok Port.