MAPE Formula:
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Definition: MAPE is a statistical measure of prediction accuracy that expresses the error as a percentage of the actual values.
Purpose: It's commonly used in forecasting to evaluate the accuracy of predictive models across different scales.
The calculator uses the formula:
Where:
Explanation: For each pair of actual and forecast values, the calculator computes the absolute percentage error, averages them, and expresses as a percentage.
Details: MAPE is widely used because it's scale-independent and easy to interpret. Lower MAPE values indicate better forecasting accuracy.
Tips:
Q1: What is a good MAPE value?
A: Generally:
Q2: What are MAPE's limitations?
A: MAPE can't handle zero actual values and penalizes negative errors more than positive ones. It's also asymmetric.
Q3: When shouldn't I use MAPE?
A: Avoid MAPE when actual values contain zeros or when the data has a natural zero point (like temperature in Celsius/Fahrenheit).
Q4: Are there alternatives to MAPE?
A: Yes, alternatives include Mean Absolute Error (MAE), Mean Squared Error (MSE), or Symmetric Mean Absolute Percentage Error (sMAPE).
Q5: How do I interpret a MAPE of 15%?
A: On average, your forecasts are off by 15% from the actual values, regardless of whether they're over or under predictions.