Forecasting Cargo Inputs to a Container Stuffing Station.
Abstract
The 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 wighted 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 1974
- Accession Number
- ADA004258
Entities
People
- Arthur Francis Shires
- James Allen Smith
Organizations
- Naval Postgraduate School