Is a Higher or Lower MAPE Better for Forecasting?
Grasp the true meaning of MAPE in forecasting accuracy. Learn how to apply and interpret this key metric for better predictions.
Forecasting plays a crucial role in business operations, guiding decisions from inventory management to financial planning. Accurately predicting future events allows organizations to prepare effectively and allocate resources efficiently. To evaluate the precision of these predictions, a widely used metric is the Mean Absolute Percentage Error, commonly known as MAPE.
What MAPE Is
MAPE quantifies the accuracy of a forecasting method by expressing the average error as a percentage. The calculation begins by finding the absolute difference between each actual data point and its corresponding forecast. This difference is then divided by the actual value, converting the error into a percentage. These individual percentage errors are then averaged across all data points in the dataset, resulting in a single percentage that represents the overall accuracy of the forecast.
This percentage-based measurement makes MAPE particularly useful for comparing the accuracy of different forecasting models or methods, even when the underlying data series have vastly different scales or units. For example, it allows for a direct comparison between a forecast of daily sales for a small retail store and monthly revenue for a large manufacturing firm.
Interpreting MAPE Results
When evaluating forecasts, a fundamental question arises: is a higher or lower MAPE more desirable? A lower Mean Absolute Percentage Error consistently indicates a more accurate forecast. Conversely, a higher MAPE suggests a greater deviation between the predicted values and the actual results, signifying less accurate predictions.
A MAPE of 0% represents a perfect forecast, where predicted values exactly match actual outcomes, though this is rarely achievable in real-world scenarios. A MAPE below 10% is often considered highly accurate in many business contexts. Values between 10% and 20% are generally seen as good, while higher percentages indicate increasing inaccuracy. However, what constitutes a “good” MAPE can vary significantly depending on the industry, the specific data being forecasted, and the inherent volatility of that data.
Context for MAPE Values
Context is essential for interpreting MAPE values in practical applications. MAPE is useful when the relative size of the error is more important than the absolute error, or when comparing forecasts across different product lines or markets with varying scales. For example, a 1,000-unit error in forecasting sales of a low-volume luxury item is far more impactful than the same error in a high-volume commodity.
The industry in question plays a significant role; a 5% MAPE in highly volatile financial markets might be exceptional, while the same percentage in a stable manufacturing process could indicate room for improvement. The presence of zero or near-zero actual values can also significantly skew MAPE, potentially leading to extremely high or undefined percentage errors, which may misrepresent the forecast’s true performance. A comprehensive understanding of the data’s characteristics and the business environment is necessary for meaningful insights.