Because of its limitations, one should use it in conjunction with other metrics.Mean Absolute Percent Error (MAPE) is a useful measure of forecast accuracy and should be used appropriately.Read about how to calculate MAD in Excel here. For example, if the actual demand for some item is 2 and the forecast is 1, the value for the absolute percent error will be |2-1| / |2| = 50%, which makes it seem like the forecast error is quite high, despite the forecast only being off by one unit.Īnother common way to measure the forecasting accuracy of a model is MAD – mean absolute deviation. A Note On Using MAPEĪlthough MAPE is straightforward to calculate and easy to interpret, there are a couple potential drawbacks to using it:ġ. Since the formula to calculate absolute percent error is |actual-forecast| / |actual| this means that it will be undefined if any of the actual values are zero.Ģ. MAPE should not be used with low volume data. The MAPE of this model turns out to be 6.47%. Step 3: Calculate the mean absolute percent error.Ĭalculate MAPE by simply finding the average of the values in column D: We will repeat this formula for each row: We will use this formula to calculate the absolute percent error for each row.Ĭolumn D displays the absolute percent error and Column E shows the formula we used: Recall that the absolute percent error is calculated as: |actual-forecast| / |actual| * 100. Step 2: Calculate the absolute percent error for each row. Step 1: Enter the actual values and forecasted values in two separate columns. To calculate MAPE in Excel, we can perform the following steps: For example, a model with a MAPE of 2% is more accurate than a model with a MAPE of 10%. The lower the value for MAPE, the better a model is able to forecast values. For example, a MAPE value of 11.5% means that the average difference between the forecasted value and the actual value is 11.5%. MAPE is commonly used because it’s easy to interpret and easy to explain. The formula to calculate MAPE is as follows: One of the most common metrics used to measure the forecasting accuracy of a model is MAPE, which stands for mean absolute percentage error.
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