a Comparison of traditional and neural networks forecasting techniques for container throughput at Bangkok Port

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a Comparison of traditional and neural networks forecasting techniques for container throughput at Bangkok Port

GOSASANG, Veerachai - Nama Orang; Watcharavee CHANDRAPRAKAIKUL - Nama Orang; Supaporn KIATTISIN - Nama Orang;

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.

Ketersediaan

IPC Corporate University Library ATC PO GOS c

ATC1600008

Tersedia

Informasi Detail

Judul Seri: -
No. Panggil: ATC PO GOS c
Penerbit: Thailand : The Asian Journal of Shipping and Logistics., 2011
Deskripsi Fisik: 20 pages
Bahasa: Indonesia
ISBN/ISSN: -
Klasifikasi: PO
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: online resource
Edisi: Volume 27 Number 3 December 2011 pp. 463-482
Subjek: Forecasting
Neural Networks
Multilayer Perceptron (MLP)
Linear Regression
Container throughput
Info Detail Spesifik: -
Pernyataan Tanggungjawab: Veerachai GOSASANG

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