Final answer:
The mean absolute deviation (MAD) tracks the average of the absolute values of forecast errors and provides a measure of how much the predicted values deviate from the actual values on average.
Step-by-step explanation:
The mean absolute deviation (MAD) tracks the average of the absolute values of forecast errors. Forecast errors refer to the differences between the predicted values and the actual values. By taking the average of these absolute values, MAD gives us a measure of how much the predicted values deviate from the actual values on average.
For example, let's say we have a forecast for the number of customers a store will have each day for a week. We compare the predicted values with the actual number of customers each day and calculate the absolute differences. The MAD would be the average of these absolute differences, telling us how much, on average, the actual number of customers deviates from the predicted number.
MAD is particularly useful in analyzing the accuracy of forecast models. A lower MAD indicates a model with less variability in its forecast errors, suggesting better accuracy. On the other hand, a higher MAD suggests a model with more variability and potentially less accuracy. By tracking the MAD over time, we can assess the performance and reliability of the forecasting model.