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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Forecast of Port Container Throughput Based on TEI@I Methodology
verfasst von : Qingfei Liu, Laisheng Xiang, Xiyu Liu
Erschienen in: Green, Pervasive, and Cloud Computing
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Abstract
Forecasting container throughput accurately is crucial to the success of any port operation policy. At present, prediction of container throughput is mainly based on traditional time series analysis or single artificial neural network technology. Recent study shows that the combined forecast model enjoys more precise forecast result than monomial forecast approach. In this study, a TEI@I hybrid forecasting model is proposed, which is based on ARIMA (autoregressive integrated moving average model) and BP neural network. Under the proposed framework, ARIMA model can be first used to predict linear component, then using BP neural network to predict the error of ARIMA model which is the nonlinear component. The new method is applied to forecasting the container throughput of Qingdao Port, one of the most important ports of China. The empirical results show that this prediction method has higher prediction accuracy than the single prediction method.