基于样本熵的港口集装箱吞吐量可预测性测度研究  

A study on sample entropy measure of predictability for container throughput of port

作　　者：李楚楚 林琴 冯宏祥 李松[1] LI Chuchu;LIN Qin;FENG Hongxiang;LI Song(Faculty of Maritime and Transportation,Ningbo University,Ningbo 315211,China;School of Nautical Studies,Universitat Politecnica de Catalunya,Barcelona 08003,Spain)

机构地区：[1]宁波大学海运学院,浙江宁波315211 [2]加泰罗尼亚理工大学航海学院,巴塞罗那08003

出　　处：《中国航海》2024年第1期81-87,共7页Navigation of China

基　　金：高等学校学科创新引智计划(“111计划”)(D21013);国家自然科学基金(72271132);浙江省基础公益研究计划(LGF18E09005)。

摘　　要：港口吞吐量历史时间序列数据具有较强的随机性,而不同特征的时间序列数据的预测精度差异较大,由此产生了时间序列数据可预测性的测度问题。学术界认为,这种可预测性可以用熵进行描述。文章采用样本熵表征测度我国20个港口集装箱吞吐量时间序列数据的复杂性,然后运用自回归综合移动平均模型(ARIMA)预测港口吞吐量。结果表明,样本熵与其预测精度之间的相关性较弱,ARIMA模型对于港口生命周期处于“成长”阶段的港口或者大型港口的预测精度更好。研究结论有助于理解熵和时间序列数据可预测性之间的关系。The measure of the predictability with historical time-series data of port throughput is one of uncertain issues caused by the strong randomness of the data.It is suggested that the entropy might be a measure for the predictability.This concept is checked out through examining the container throughput time-series data from 20 ports in China.SampEn(Sample Entropy)is used to measure the complexity of the time series data and the ARIMA(Autoregressive Integrated Moving Average)model is used to calculated the container throughput prediction of the port.The research shows that the correlation between SampEn and the predictability is rather weak and the ARIMA model is only good for large ports or growing ports.

关 键 词：集装箱吞吐量 样本熵 自回归综合移动平均模型 生命周期 

分 类 号：U692.3[交通运输工程—港口、海岸及近海工程]