Which input component is essential for the mean absolute percentage deviation function?

The mean absolute percentage deviation (MAPD) function is designed to gauge the accuracy of forecasts by comparing the forecasted values to actual historical data. This function relies on the statistical evaluation of forecast accuracy, which requires outputs of statistical functions to determine how well the forecasts performed against the actual historical performance.

The correct component is crucial because it provides the necessary statistical metrics, such as errors between the forecasted and actual values, which are fundamental in calculating the mean absolute percentage deviation. By using these statistical outputs, one can analyze the forecasts' variances and understand their reliability.

In this context, while other components such as historical demand records provide the actual values needed for comparison, they do not directly supply the statistical outputs essential for calculating the MAPD. Similarly, static value tables and calculated forecast values are not sufficient on their own to perform the necessary statistical functions, as they lack the analytical capability required for determining forecast accuracy metrics. Thus, utilizing the output of statistical functions becomes paramount in accurately computing the mean absolute percentage deviation.