Mean absolute percentage error

Definition

Mean absolute percentage error (MAPE) is a statistical measure used to assess the accuracy of a forecasting method by calculating the average absolute percentage error between forecasted values and actual values. It is particularly useful in time series analysis because it provides a clear indication of the forecast's accuracy in relative terms, allowing for easy comparison across different datasets or forecasts. MAPE is valuable for measuring forecast accuracy as it helps identify how well forecasting models perform over time, especially when trends or seasonal patterns are present.

5 Must Know Facts For Your Next Test

1. MAPE is calculated by taking the absolute difference between actual and forecasted values, dividing by actual values, and then averaging those percentages over all observations.
2. A lower MAPE value indicates better forecasting accuracy, with a value of 0% representing a perfect forecast.
3. MAPE can be misleading when actual values are very close to zero, as it can lead to extremely high percentage errors.
4. Because MAPE expresses accuracy in percentage terms, it is especially useful when comparing forecast performance across different scales or units.
5. In practice, MAPE is commonly used in various industries, including finance, supply chain management, and demand forecasting, to evaluate the effectiveness of forecasting methods.

Review Questions

• How does mean absolute percentage error help evaluate the effectiveness of different forecasting methods?
  • Mean absolute percentage error (MAPE) helps evaluate the effectiveness of different forecasting methods by providing a standardized metric that expresses forecast accuracy in percentage terms. This allows for easy comparison across various datasets and forecasting techniques. By calculating the average absolute percentage error between forecasted and actual values, MAPE enables analysts to quickly identify which methods perform better in predicting outcomes over time.
• What are the limitations of using mean absolute percentage error as a measure of forecast accuracy?
  • While mean absolute percentage error (MAPE) is widely used, it has some limitations. One major drawback is its sensitivity to values close to zero; small actual values can lead to disproportionately high percentage errors, skewing the results. Additionally, MAPE does not account for the direction of errors—whether forecasts are consistently too high or too low—which could provide further insights into forecasting performance. As a result, it may be beneficial to consider other accuracy measures alongside MAPE for a more comprehensive evaluation.
• Evaluate how mean absolute percentage error can be applied in different industries and its impact on decision-making processes.
  • Mean absolute percentage error (MAPE) has broad applications across various industries such as finance, retail, and logistics, where accurate forecasting is critical for effective decision-making. For example, in retail, MAPE helps businesses assess inventory demand forecasts to optimize stock levels and reduce costs. In finance, accurate forecasts can influence investment strategies. By providing a clear measure of forecast accuracy in percentage terms, MAPE assists organizations in identifying reliable forecasting models, ultimately leading to better resource allocation and improved overall performance.

Related terms

Forecasting: The process of estimating future values based on historical data and analysis of trends, patterns, and other variables.

Absolute Error: The difference between the actual value and the forecasted value, without considering whether the error is positive or negative.

Mean Squared Error: A measure of forecast accuracy that squares the errors between predicted and actual values before averaging them, emphasizing larger errors.