Abstract
ABSTRACT The purpose of this study was to determine which forecasting model would most accurately predict meal participation at university residential dining facilities. Forecasting techniques including naïve, moving average (3 versions), and simple exponential smoothing were applied to data collected from two dining halls over 15 weeks. An analysis of the forecasting models using Mean Absolute Deviations (MAD), Mean Squared Errors (MSE), and Mean Absolute Percentage Errors (MAPE) indicated that simple mathematical forecasting techniques provided better predictions (MAPE ≤ 6.4%) than the naïve method in all cases studied. Moving average methods outperformed other methods 83% of the time. Implications are discussed.
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