Abstract: Abstract: Considering the Chinese port present conditions and port throughput prediction theory, cluster-Vector Auto-Regression(VAR) sub-category of goods throughput prediction model is set up by analyzing Chinese coastal port cargo composition. It improves the prediction precision and accuracy for Chinese coastal port throughput, which provides more reliable reference for investment planning, etc. The main characteristics of this model are: Firstly, the capacity of Chinese coastal ports is analyzed and forecasted from the port throughput composition. It solves the pitfall that existing literature doesn’t mine information deep enough due to choosing port throughput or a small number of macroeconomic indicators, such as GDP time series prediction variables for the forecast. Secondly, with considering the coordinative relationship between major coastal port cargo category throughputs, the coastal port cargo category throughputs index is treated by using cluster analysis method. The maximum information is retained to ensure the accuracy of prediction model. Thirdly, the model is convenient and flexible, which could be extended to the throughput prediction for a single port or port group, or even extended to a wider range of the prediction problem possessing more time series indicators.

Key words: coastal port, throughput, VAR, cluster analysis, prediction model