Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/329715 
Year of Publication: 
2023
Citation: 
[Journal:] Asian Journal of Shipping and Logistics (AJSL) [ISSN:] 2352-4871 [Volume:] 39 [Issue:] 2 [Year:] 2023 [Pages:] 67-77
Publisher: 
Elsevier, Amsterdam
Abstract: 
Forecasting container throughput is critical for improved port planning, operations, and investment strategies. Reliability of forecasting methods need to be ensured before utilizing their outcomes in decision making. This study compares forecasting performances of various time series methods, namely autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), Holt-Winter's Exponential Smoothing (HWES), and the Prophet model. Since forecast combinations can improve performance, simple and weighted combinations of ARIMA, SARIMA and HWES have been explored, too. Monthly container throughput data of port of Shanghai, Busan, and Nagoya are used. The Prophet model outperforms others in the in-sample forecasting, while combined models outperform others in the out-sample forecasting. Due to the observed differences between the in-sample and out-sample forecast accuracy measures, this study proposes a forecast performance metric consistency check approach for informed real-world applications of forecasting models in port management decision-making.
Subjects: 
Exponential smoothing
Forecast combination
Port throughput
Time series prediction
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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