Symmetric mean absolute percentage error

Definition

Symmetric mean absolute percentage error (sMAPE) is a measure used to evaluate the accuracy of forecasting models by calculating the average absolute percentage error between predicted and actual values, adjusted to ensure symmetry. This metric is particularly useful as it provides a more balanced view of errors, treating overestimations and underestimations equally, which is vital for accurate forecasting in various industries.

5 Must Know Facts For Your Next Test

1. sMAPE is calculated using the formula $$sMAPE = \frac{100}{n} \sum_{t=1}^{n} \frac{|F_t - A_t|}{\frac{|F_t| + |A_t|}{2}}$$, where F is the forecasted value and A is the actual value.
2. This metric is particularly favored in contexts where both over-predictions and under-predictions are important, ensuring that they are equally weighted in the assessment of forecast accuracy.
3. sMAPE can be less sensitive to large values compared to other metrics, which makes it suitable for datasets with varying scales.
4. Unlike traditional percentage error metrics, sMAPE avoids issues related to division by zero when actual values are zero by taking an average of both forecasted and actual values in the denominator.
5. An ideal sMAPE score is 0%, indicating perfect accuracy, while scores above this represent increasing levels of forecast error.

Review Questions

• How does symmetric mean absolute percentage error improve upon traditional percentage error measures?
  • Symmetric mean absolute percentage error enhances traditional measures by ensuring that overestimations and underestimations are treated equally, which helps in providing a more balanced assessment of forecast accuracy. This is achieved by calculating errors as a function of both predicted and actual values, making sMAPE particularly effective in scenarios where both types of errors carry significant implications. Additionally, sMAPE reduces biases that may arise from large values dominating the error calculation.
• In what situations would using symmetric mean absolute percentage error be more beneficial than using mean absolute percentage error?
  • Using symmetric mean absolute percentage error is particularly beneficial in contexts where both over- and under-forecasting errors have similar consequences or impact on decision-making. For instance, in inventory management or financial forecasting, an underestimation may lead to stockouts while an overestimation could result in excess inventory. By employing sMAPE, forecasters can ensure that they are equally concerned with both types of errors, leading to more reliable forecasting strategies.
• Evaluate how symmetric mean absolute percentage error can influence decision-making in business forecasting.
  • Symmetric mean absolute percentage error can significantly influence decision-making in business forecasting by providing a clearer understanding of forecast reliability. By quantifying errors in a symmetric manner, businesses can make more informed decisions regarding inventory levels, resource allocation, and strategic planning. This metric helps identify models that consistently perform well regardless of fluctuations in actual values, allowing businesses to adopt forecasting methods that minimize risk and enhance operational efficiency. Ultimately, utilizing sMAPE can lead to improved overall performance and better alignment with market demands.

Related terms

Mean Absolute Percentage Error:

Mean Absolute Percentage Error (MAPE) measures the accuracy of a forecasting method by averaging the absolute percentage errors of individual forecasts, providing a straightforward way to understand forecast performance.

Forecast Accuracy: Forecast accuracy refers to the degree to which a forecasted value aligns with the actual observed value, often measured using various metrics like MAPE or sMAPE.

Absolute Error: Absolute error is the difference between the actual value and the forecasted value, without considering the direction of the error, providing a straightforward measure of prediction deviation.