.svg)
What is Mean Absolute Percentage Error?
The Mean Absolute Percentage Error (MAPE) is a statistical measure used to evaluate the accuracy of forecasting models. It calculates the average absolute percentage difference between predicted and actual values, offering a clear perspective on prediction precision.
Expressed as a percentage, MAPE is easy to interpret and compare across various datasets or models, making it a valuable tool in fields such as finance, supply chain management, and weather forecasting. While simple and clear, MAPE has limitations, particularly when dealing with zero or near-zero actual values, leading to skewed or undefined results.
How Do You Calculate Mean Absolute Percentage Error?
The formula for Mean Absolute Percentage Error involves subtracting the forecasted value from the actual value, dividing by the actual value, and then averaging these absolute percentage errors across all data points. Multiplying by 100 converts this into a percentage, providing insights into forecast accuracy.
Limitations of MAPE
- Zero Values: MAPE struggles with zero values, leading to undefined results. Analysts may need to consider alternative metrics or adjustments in such cases.
- Asymmetry: MAPE may weigh over-predictions and under-predictions differently, potentially leading to biased interpretations of accuracy.
- Not Suitable for All Applications: In contexts like intermittent demand forecasting, MAPE's reliability can diminish, especially with values close to zero.
- Scale Dependency: MAPE's effectiveness can be influenced by data scale, making it challenging to compare datasets with differing scales.
- Overemphasis on Errors: Larger errors may overshadow smaller, more frequent ones, skewing overall error analysis.
Mean Absolute Percentage Error and Model Monitoring
In model monitoring, particularly in predictive modeling, MAPE serves as a key indicator of prediction accuracy. It evaluates prediction proximity to actual outcomes, providing a clear percentage measure of error. This is particularly useful in scenarios requiring precise predictions, such as financial forecasting and inventory management.
