An Optimal Forecasting Method of Passenger Traffic in Greek Coastal Shipping

I. Sitzimis
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引用次数: 2

Abstract

Purpose: The main goal of this study is to exact an optimal forecasting method by answering the research question: which is the best model for capturing short-term seasonal components of passenger traffic in Greek coastal shipping? Design/methodology/approach: There are not a lot of scientific efforts in forecasting passenger traffic in Greece. In order to fill this gap, we tried to find an optimal forecasting method, by comparing Box-Jenkins ARIMA, smoothing and decomposition methods. As Greek coastal shipping consists of several concentrated submarkets (lines) we remained in fourteen popular itineraries (including total passenger traffic). Taking into consideration the high seasonality and no stationarity that characterizes those routes we limited our analysis to Winter’s triple exponential smoothing, to time series decomposition method, to simple seasonal model and to seasonal ARIMA models. Findings: The analysis results show that in fourteen popular coastal routes Winters’ multiplicative method, simple seasonal model and decomposition multiplicative trend and seasonal model have the best integration to the time series data. No coastal line led to better results by seasonal Box-Jenkins ARIMA models. Research limitations/implications: The results should be treated with caution since COVID-19 pandemic does not allow safe conclusions for the forecasting period 2020-2022 in GCS. However, the forecasting results of the first quarter of 2020, when pandemic had not fully prevailed, gave encouraging results with little deviations between predicted and actual values. Originality/value: Greek coastal shipping is one of the biggest in Europe serving a large number of passengers and having a large part of the total shipping fleet. It plays an important role for Greek economy and society, as it connects the majority of inhabited islands to mainland. The finding of an optimal forecasting method of passenger traffic is very significant for both business and government policy. Decisions on the number of routes served by shipping companies, on ships by coastal line (number and size), on companies' pricing policy, on public service obligations, on state port infrastructure policy and on the amount of state funding for barren lines are typical examples.
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希腊沿海航运客运量最优预测方法
目的:本研究的主要目的是通过回答研究问题:哪一个模型是捕捉希腊沿海航运客运量短期季节性成分的最佳模型,从而确定一种最佳预测方法?设计/方法/方法:在预测希腊的客流量方面没有太多的科学努力。为了填补这一空白,我们通过对比Box-Jenkins ARIMA、平滑和分解方法,试图找到一种最优的预测方法。由于希腊沿海航运由几个集中的子市场(线路)组成,我们保持了14条热门路线(包括总客流量)。考虑到这些路线的高季节性和无平稳性特征,我们将分析限制在Winter的三重指数平滑,时间序列分解方法,简单季节性模型和季节性ARIMA模型。结果:分析结果表明,在14条热门沿海航线中,温特斯乘法法、简单季节模型和分解乘法趋势与季节模型对时间序列数据的整合效果最好。没有海岸线导致季节性Box-Jenkins ARIMA模型的结果更好。研究局限性/意义:由于COVID-19大流行无法在GCS中对2020-2022年的预测期得出安全结论,因此应谨慎对待结果。然而,在大流行尚未完全流行的2020年第一季度的预测结果令人鼓舞,预测值与实际值之间的偏差很小。独创性/价值:希腊沿海航运是欧洲最大的航运之一,为大量乘客提供服务,占总船队的很大一部分。它在希腊经济和社会中扮演着重要的角色,因为它连接了大多数有人居住的岛屿和大陆。寻找最优的客运量预测方法对企业和政府决策都具有重要意义。关于航运公司服务的航线数量、沿海航线的船舶数量(数量和大小)、公司定价政策、公共服务义务、国家港口基础设施政策和国家对贫瘠航线的资金数额的决定都是典型的例子。
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审稿时长
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