What Is a MAPE & How Is It Used for Forecasting?

The Mean Absolute Percentage Error (MAPE) is a widely used metric to assess the accuracy of predictions. It provides a clear, understandable measure of how much a forecast deviates from actual outcomes, helping businesses make informed decisions. As a percentage, MAPE offers a standardized way to compare different forecasting methods. This metric helps identify areas where prediction models need improvement, leading to more reliable future planning. Its intuitive nature makes it a popular choice for communicating forecast accuracy to a broad audience.

Understanding Mean Absolute Percentage Error

Mean Absolute Percentage Error (MAPE) quantifies forecast accuracy by expressing the error as a percentage of the actual value, breaking down into absolute error, percentage error, and mean. Absolute error is the raw difference between forecasted and actual values. Percentage error converts this difference into a proportion of the actual value, allowing for comparison across different scales. For example, if a forecast is off by 10 units and the actual value was 100, the percentage error is 10%. The mean is the average of these individual percentage errors across a series of forecasts.

MAPE is a common evaluation metric in regression problems and forecasting models due to its intuitive interpretation. It provides a straightforward measure of how far off predictions are on average. This makes MAPE a useful tool for gauging forecasting model performance, especially in time series analysis.

Calculating Mean Absolute Percentage Error

Calculating the Mean Absolute Percentage Error involves a few distinct steps, culminating in a single percentage value that represents average forecast accuracy. The formula for MAPE is:

MAPE = (1/n) Σ (|Actual – Forecast| / |Actual|) 100%

Here, ‘n’ represents the number of data points, ‘Actual’ is the observed value, and ‘Forecast’ is the predicted value. The summation symbol (Σ) indicates this calculation is performed for each data point, and the results are then summed.

To illustrate the calculation, consider a simple example with three data points:
| Period | Actual Value (A) | Forecast Value (F) |
| :—– | :————— | :—————– |
| 1 | 100 | 95 |
| 2 | 200 | 210 |
| 3 | 150 | 145 |
For Period 1, the absolute percentage error is (|100 – 95| / |100|) 100% = 5%. For Period 2, it is (|200 – 210| / |200|) 100% = 5%. For Period 3, the absolute percentage error is (|150 – 145| / |150|) 100% ≈ 3.33%. Summing these individual percentage errors yields 13.33%. Divide this sum by the number of data points (3) to get the MAPE. Thus, MAPE = 13.33% / 3 ≈ 4.44%. This indicates that, on average, forecasts were approximately 4.44% off from actual values.

Interpreting MAPE Values

Interpreting MAPE values provides direct insight into forecast accuracy. A lower MAPE value generally signifies higher forecast accuracy. For instance, a MAPE of 5% suggests that, on average, predictions deviate from actual values by 5%.

There is no universally accepted “good” or “bad” MAPE value; its interpretation often depends on the specific industry, data volatility, and forecast context. What might be considered acceptable in manufacturing (20% to 40%) could be too high in pharmaceuticals (10% to 20% in stable environments). Industry benchmarks and data nature play a significant role in determining a satisfactory MAPE.

A MAPE of 0% indicates perfect prediction, though rarely achieved. Values between 0% and 10% typically suggest excellent forecast accuracy, while 10% to 20% is often considered good. A MAPE exceeding 20% may indicate fair to moderate accuracy, depending on the specific application.

Common Applications of MAPE

Mean Absolute Percentage Error finds widespread application across various business functions where accurate forecasting is paramount. It is commonly used in areas such as sales forecasting, demand planning, and inventory management. In these contexts, MAPE helps businesses assess how well their predictions align with actual customer demand or sales figures.

MAPE is also a valuable metric in financial forecasting and budgeting. Companies use it to evaluate the accuracy of revenue projections, expense estimates, and other financial outlooks, allowing for better financial controls.

Its intuitive format makes it easy to communicate forecast performance to stakeholders, regardless of their technical background. This clarity aids in decision-making regarding resource allocation, production schedules, and overall strategic planning.