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.
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Acknowledgements
This work is supported by the Natural Science Foundation of China (nos. 61472231, 61502283, 61640201), Ministry of Education of Humanities and Social Science Research Project, China (12YJA630152), Social Science Fund Project of Shandong Province, China (16BGLJ06, 11CGLJ22).
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Liu, Q., Xiang, L., Liu, X. (2019). Forecast of Port Container Throughput Based on TEI@I Methodology. In: Li, S. (eds) Green, Pervasive, and Cloud Computing. GPC 2018. Lecture Notes in Computer Science(), vol 11204. Springer, Cham. https://doi.org/10.1007/978-3-030-15093-8_32
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DOI: https://doi.org/10.1007/978-3-030-15093-8_32
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