A comparison of ARIMA and ANN techniques in predicting port productivity and berth effectiveness

Article history: Received: October 2, 2018 Received in revised format: October 20, 2018 Accepted: November 29, 2018 Available online: November 30, 2018 Business process evaluation is a common norm in small-medium-large industries globally and information obtained during such evaluation have been used in simulating the future performance of most industries using mathematical models such as Autoregressive Integrated Moving Average (ARIMA) and artificial neural network (ANN). This study explored the possibility of predicting port productivity and berth effectiveness of a seaport using ANN and ARIMA. A comparative analysis of a multi-layer perceptron (MLP) back propagation algorithm and ARIMA performance was carried out based on ships days at port, days at berth and tonnage: the model’s input parameters, while port productivity and berth effectiveness were the model outputs. The MLP-ANN and ARIMA (1, 0, 4port productivity) and (1, 0, 4-berth effectiveness) results were compared based on their coefficient of correlation and mean square error. The coefficient of correlation for port productivity prediction using MLP-ANN was 0.998. This value outperformed that of ARIMA (0.9862) for port productivity; berth effectiveness coefficients of correlation of 0.9956 and 0.9928 were obtained using the MLP-ANN and the ARIMA models, respectively. © 2019 by the authors; licensee Growing Science, Canada.

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