Time Series Forecasting • Model Evaluation (MAPE, RMSE, Forecast Bias)Easy⏱️ ~2 min
What is Mean Absolute Percentage Error (MAPE) in Forecasting?
Mean Absolute Percentage Error (MAPE) measures forecast accuracy as a percentage of the actual value. For each prediction, you calculate the absolute difference between actual and forecast, divide by the actual value, then average across all observations and express as a percentage. A MAPE of 12% means your predictions are off by 12% on average.
The key advantage is that MAPE is scale free and intuitive. You can compare forecast accuracy across products with completely different magnitudes. An Amazon retailer can use a single MAPE threshold to evaluate both high volume electronics (selling thousands daily) and niche accessories (selling tens weekly). Executives understand "off by 15%" immediately without needing domain context.
However, MAPE has critical failure modes. It becomes infinite when actual values are zero, which is common in intermittent demand for spare parts or long tail SKUs. When actuals are small, MAPE inflates dramatically. If actual = 1 and forecast = 5, that's a 400% error, and a handful of such points can dominate your mean. More subtly, MAPE systematically rewards under forecasting because lower predictions reduce the numerator while the denominator stays fixed at the actual value.
In practice, high volume SKUs at major retailers achieve MAPE of 8 to 15% at one week horizon and 15 to 25% at 8 to 13 weeks out. Long tail SKUs often exceed 40% MAPE at longer horizons. Teams typically use MAPE for executive dashboards and portfolio comparisons but avoid optimizing models directly against it to prevent conservative forecast bias.
💡 Key Takeaways
•MAPE is calculated as the average of absolute percentage errors: sum of |actual minus forecast| divided by |actual|, expressed as percentage
•Scale free comparison allows using single thresholds across products with different volumes, from thousands to single units daily
•Fails catastrophically with zero actuals (infinite error) and inflates dramatically with small actuals, making it unsuitable for intermittent demand
•Systematically favors under forecasting because the denominator is fixed at actual value, so lower forecasts can reduce the error ratio
•Typical production values: 8 to 15% MAPE at one week horizon for high volume SKUs, 15 to 25% at 8 to 13 weeks, over 40% for long tail items
•Best used for executive dashboards and portfolio comparison, but dangerous as a direct optimization target due to conservative forecast bias
📌 Examples
Amazon retailer with 50 million SKU location pairs: Uses MAPE for business owner dashboards to communicate forecast quality across all categories without explaining units or magnitudes
E-commerce forecasting: High velocity electronics achieve 10% MAPE at one week, while seasonal accessories hit 30% at same horizon due to demand volatility
Metric gaming incident: Retail team improved MAPE from 22% to 18% by clipping high forecasts, but fill rate on A movers dropped 3 percentage points due to increased stockouts