GMDH-based hybrid model for container throughput forecasting: Selective combination forecasting in nonlinear subseries

Abstract

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.