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We study historical data of the cargo going through the Guangzhou (GZ) port and related research the relationships between cargo shipments through the GZ port and its relation to domestic and international shipping prices (rates).In turn, we develop a regression based forecasting model based on the data of the GZ cargo port. The second task is to introduce the GZ port, the international dry bulk shipping market; the Chinese coast bulk freight index (CCBFI); and the Baltic dry index (BDI) which reflect domestic and international freight rates respectively. The third task is to make use of the data of the GZ cargo port, CCBFI and BDI from January 2004 to February 2010. The developed model establishes a multi-linear regression to relate the impact of the previous month BDI and CCBFI on the current GZ port cargo and determine the magnitude of the effect. Second, we establish a time series-regression forecasting model. This requires us to observe and consider including historical data of BDI, CCBFI and GZ cargo and come to a conclusion that relates the impact of BDI and CCBFI on the GZ cargo port. Finally, by developing a two parameter exponentially weighted moving average (EWMA), we obtain forecast with high predictive accuracy.

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