摘要
近年来,中国港口集装箱吞吐量增长迅速.如何准确预测中国港口集装箱吞吐量是一个极其重要且具有挑战性的问题.采用一种自适应方法来解决该问题,即Yang（2004）提出的指数加权聚合预测（AFTER）.我们采用该方法将两种时间序列模型：SARIMA和VAR进行组合.对中国七大港口的预测结果表明,AFTER方法比常用的简单平均预测具有优势,它通常能以更高的频率自动设置更大的权重在更好的个体预测上.
Recently, Chinese ports container throughput increased rapidly. Accurate forecasting for container throughput of Chinese posts is an important and challengeable problem. We apply an adaptive approach-Aggregated forecast through exponential reweighting（AFTER）, introduced by Yang（2004） to this problem. Two classes of time series models（seasonal ARIMA model and vector autoregressive model）are combined. The results indicated that the AFTER approach has advantage over the common used simply average forecasting, and can automatically put a large weight on the better individual forecast with high frequency.
作者
赵尚威
周建红
ZHAO Shangwei;ZHOU Jianhong(College of Science, Minzu University of Chinese, Beijing 100081;Department of Credit Management, Guangdong University of Finance, Guangzhou 510521)
出处
《系统科学与数学》
CSCD
北大核心
2018年第2期210-219,共10页
Journal of Systems Science and Mathematical Sciences
基金
教育部人文社会科学研究青年基金项目(15YJC630013)
教育部人文社会科学研究青年基金项目(17YJC910011)
中央民族大学自主科研项目青年学术团队引领计划(2017MDYL21)
青年教师科研能力培育项目(2017QNPY34)资助课题
关键词
自适应组合
集装箱吞吐量
预测
时间序列模型
Adaptive combination, container throughput, forecasting, time series model.
