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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/26113

Title: 季節性預測模式比較—以台灣地區國際港埠進口貨櫃預測為例
The ComparisStudy on the Prediction of Imported Contaon of the Seasonal Forecasting Models-A iner Volume for International Container Ports in Taiwan
Authors: 彭文怡;朱經武
Wen-Yi Peng;Ching-Wu Chu
Contributors: NTOU:Department of Shipping and Transportation Management
Keywords: 古典分解法;三角函數迴歸;季節性虛擬變數;灰預測
Classical Decomposition;Trigonometric Model;Regression Model with Seasonal Dummy Variables;and Grey Forecast
Date: 2006-06-01
Issue Date: 2011-10-20T08:33:47Z
Publisher: 航運季刊
Maritime Quarterly
Abstract: 摘要:港埠之貨櫃量為港埠營利之主要來源,因此預測港埠未來之貨櫃運量是港埠規劃、興建及管理之重要依據,就各港近四年每月的進口貨櫃量資料顯示,含有季節性波動之趨勢,適用各港進行未來短期之預測。本研究之目的為比較四種常見使用之單一變數預測方法,有古典分解法、三角函數迴歸、季節性虛擬變數及灰預測,利用實證分析,以驗證何者可提供預測進口貨櫃量最佳之精確度。研究對象以台灣地區三大國際港埠(基隆港、台中港與高雄港)進口之貨櫃量,測試所得之最佳預測方法是否相同。經過平均絕對誤差(MAE)、平均絕對誤差百分比(MAPE)及殘差均方根(RMSE)等評估指標比較後發現,不論採何種指標,基隆港以古典分解法,台中港以古典分析法與灰預測,高雄港以灰預測的預測能力最佳。未來可加入其他的預測模式,以驗證港埠進口貨櫃量之準確度,並驗證進、出口所使用之最佳預測方法是否相同。
Abstract:To measure the profitability of a container port is based on the total volume handled in the harbor. In order to properly design, construct and manage a port, it is necessary to predict a port's expected volume in the port for the future. From the figures of monthly import volume of each container port in the recent four months, the trend of seasonal fluctuation was found. The data could be used to appropriately make a prediction on the volume of each port in the short-term. The purpose of this paper is to compare the accuracy of four forecasting models, i.e. Classical Decomposition, the Trigonometric Model, the Regression Model with Seasonal Dummy Variables, and the Grey Forecast, for the import volume of the international container ports in Taiwan. By using the method of verification on the actual data collected, we are able to prove which prediction model can provide the best accuracy. The research objective is set on the import volume of the three international ports of Keelung, Taichung and Kaohsiung. The testing data is derived from the monthly statistics of the import volume from Jan. 2001 to Dec. 2004. By comparing the findings based on the revaluation method, Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE) and Root Mean Squared Error (RMSE), the Classical Decomposition provided the most accurate predictions on the port of Keelung, where the Classical Decomposition and the Grey Forecast provided the most accurate predictions on Taichung, and the Grey Forecast provided the most accurate predictions on Kaohsiung ports. Different prediction mythologies each have their own merits and weaknesses, but, to be more practical, we have to find the most suitable method to fit our particular marine shipping industry needs of forecasting accuracy. Therefore, it is suggested to proceed studying the same outbound container volume, in order to find out whether the same result is obtainable as the study on imported cargo volume.
Relation: 15(2), pp.21-36
URI: http://ntour.ntou.edu.tw/handle/987654321/26113
Appears in Collections:[航運管理學系] 期刊論文

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