GMDH-based hybrid model for container throughput forecasting: Selective combination forecasting in nonlinear subseries
GMDH-based hybrid model for container throughput forecasting: Selective combination forecasting in nonlinear subseries
复制标题
基于GMDH的集装箱吞吐量预测混合模型：非线性子系列中的选择性组合预测
DOI：
10.1016/j.asoc.2017.10.033
复制
发表时间：
2018-01-01
影响因子：
8.7
通讯作者：
Xiao, Jin
中科院分区：
文献类型：
--
作者：
Mo, Lili;Xie, Ling;Xiao, Jin
准确预测未来集装箱吞吐量对港口的建设、升级和运营管理具有重要意义。引入数据处理的分组方法（GMDH）神经网络，提出了一种基于GMDH的集装箱吞吐量混合预测模型（HFMG）。该模型将原始集装箱吞吐量序列分解为线性趋势和非线性变化两部分，并采用季节性自回归积分移动平均（SARIMA）方法对线性趋势进行预测。考虑到非线性子序列预测的复杂性，该模型采用支持向量回归（SVR）、反向传播（BP）神经网络和遗传规划（GP）3种非线性单一模型对非线性子序列进行预测。然后，该模型通过GMDH神经网络对非线性子序列建立选择性组合预测，并得到其组合预测结果。最后，将两部分的预测结果进行整合，得到集装箱吞吐量原始时间序列的预测结果。利用厦门港和上海港集装箱吞吐量数据进行实证分析，结果表明HFMG模型的预测性能优于SARIMA模型以及SARIMA-SVR、SARIMA-GP、SARIMA-BP等混合预测模型。最后，给出了2016年全年两港集装箱吞吐量的月度样本外预测。(C)2017爱思唯尔B. V.保留所有权利。
The accurate forecasting of future container throughput is important for the construction, upgrade, and operation management of a port. This study introduces group method of data handling (GMDH) neural network and proposes a hybrid forecasting model based on GMDH (HFMG) to forecast container throughput. This model decomposes the original container throughput series into two parts: linear trend and nonlinear variation, and uses the seasonal autoregressive integrated moving average (SARIMA) approach to predict the linear trend. Considering the complexity of forecasting nonlinear subseries, the proposed model adopts three nonlinear single models, namely, support vector regression (SVR), back-propagation( BP) neural network, and genetic programming (GP), to predict the nonlinear subseries. Then, the model establishes selective combination forecasting by the GMDH neural network on the nonlinear subseries and obtains its combination forecasting results. Finally, the predictions of two parts are integrated to obtain the forecasting results of the original container throughput time series. The container throughput data of Xiamen and Shanghai Ports in China are used for empirical analysis, and the results show that the forecasting performance of the HFMG model is better than that of SARIMA model, as well as some hybrid forecasting models, such as SARIMA-SVR, SARIMA-GP, and SARIMA-BP. Finally, the monthly-out-of-sample forecasts of container throughput for the two ports throughout 2016 are given. (C) 2017 Elsevier B.V. All rights reserved.