Management Review

›› 2018, Vol. 30 ›› Issue (1): 195-201.

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Port Throughput Forecasting Model Based on Context Change

Lu Bo1,2, Yang Xianfei3, Wang Shouyang4   

  1. 1. International College, Dalian University, Dalian 116622;
    2. National Joint Engineering Laboratory for Intelligent Shipping and Logistics Network Technology, Dalian University, Dalian 116622;
    3. School of Economics and Management, Dalian University, Dalian 116622;
    4. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190
  • Received:2017-02-16 Online:2018-01-28 Published:2018-01-24

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Abstract:

Context change is an important factor that affects the forecasting of port throughput. However, existing researches on the modeling of port throughput forecasting are relatively less concerned about this issue. A dynamic penalty support vector regression model is proposed in this paper. By dynamically adjusting the penalty coefficient of each data, this model is quickly adapted to the development of things under the new contexts and improves prediction accuracy. An empirical research is carried out based on annual cargo throughput data of Dalian Port and Tianjin port in the period from 1980 to 2014, and a comparison is made with traditional support vector regression model and ARIMA model. Experimental results show that this model has the following two characteristics compared with the other two regression models:first, when the context change, the model can quickly adapt to new context, so as to improve the accuracy of prediction; second, the prediction performance of the model is more accurate and stable, so it is able to improve the practicality.

Key words: context change, port throughput, time series prediction, dynamic penalty, support vector regression model