Forecasting cargo inputs to a container stuffing station
Smith, James Allen
Shires, Arthur Francis
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This thesis investigates various techniques for forecasting the volume of containerizable cargo that flows into the container stuffing station at the Military Ocean Terminal, Bay Area, Oakland, California. Cargo input data is analyzed in terms of weekly cargo volume inputs for a selected number of major ports of debarkation. The time-series data for these ports is first tested for serial correlation. Based on the affirmative results of the serial correlation test, the following forecasting methods are investigated: the moving average, the exponentially weighted average, the exponentially weighted average with trend adjustment and the exponentially weighted average with an adaptive response rate. By means of statistical testing procedures, the "best" forecasting method is determined.
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