For any forecasting technique, it is important to measure the accuracy of its forecasts. Forecasts almost always contain errors. Random error results from unpredictable factors that cause the forecast to deviate from the actual demand. Forecasting analysts try to minimize forecast errors by selecting appropriate forecasting models, but eliminating all forms of errors is impossible.

Forecast error for a given period `t` is simply the difference found by subtracting the forecast from actual demand, or

$${E}_{t}={D}_{t}-{F}_{t}$$

where

$$\begin{array}{l}{E}_{t}=\text{forecasterrorforperiod}t\text{}\\ {D}_{t}=\text{actualdemandforperiod}t\text{}\\ {F}_{t}=\text{forecastforperiod ...}\end{array}$$

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