Three types of forecast error metrics

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Operations Management Planning Background Information (Continued) Forecast Errors All forecasts are subject to error. Understanding the nature and size of errors is important to making good decisions. Forecast error is the difference between the observed value of the time series and the forecast, or At - Ft. Take a look at the table seen in the chart below. Suppose that a forecasting method provided the forecasts in column E of the sample chart. The forecast errors are computed in column F. Because of the inherent inability of any model to forecast accurately, we use quantitative measures of forecast accuracy to evaluate how well the forecasting model performs. Clearly, we want to use models that have small forecast errors. Generally, three types of forecast error metrics are used: Mean square error (MSE), Mean absolute deviation (MAD) and Mean absolute percentage error (MAPE).

Reference no: EM133679894

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