Measure average forecast bias with flexible inputs. Review errors, accuracy metrics, and export reports fast. Use clear results to tune future statistical forecasts confidently.
Result Summary
Dataset: Monthly Demand Forecast
Interpretation: Positive MFE means actual values are higher than forecasts on average.
Mean Forecast Error: Sum of signed forecast errors / Number of paired observations
MAE: Sum of absolute errors / Number of paired observations
MSE: Sum of squared errors / Number of paired observations
RMSE: Square root of MSE
MAPE: Average of absolute percentage errors
Tracking Signal: Total signed error / Mean absolute error
How to Use This Calculator
Enter a name for your dataset.
Select the error direction used in your report.
Paste actual values in the first box.
Paste matching forecast values in the second box.
Choose decimal places for the final output.
Press the calculate button.
Review MFE, MAE, RMSE, MAPE, and tracking signal.
Use CSV or PDF buttons to save the result.
Understanding Mean Forecast Error
Mean Forecast Error, or MFE, measures average forecast bias. It compares each forecast with its matching actual value. The calculator uses every paired period. Then it averages the signed errors. A positive or negative result shows direction. That direction is the main reason MFE is useful.
Why Bias Matters
Forecast accuracy is not only about size of error. A model can look accurate and still lean high or low. MFE exposes that lean. If the average error is near zero, the forecast is balanced. If it is far from zero, the model may need adjustment. Businesses use this insight for demand, stock, staffing, sales, finance, and production planning.
How This Tool Helps
This calculator accepts pasted series data. You can enter actual values and forecast values line by line. You may also use commas or spaces. The tool checks count matching. It reports the signed error for each period. It also gives MAE, MSE, RMSE, MAPE, total error, and tracking signal. These extra metrics help explain the size and stability of error.
Reading the Results
MFE should be read with the selected error direction. With actual minus forecast, a positive MFE means under forecasting. Actual values were higher than forecasts. A negative MFE means over forecasting. With forecast minus actual, the meaning reverses. Always document the convention before sharing results. That avoids confusion in reports.
Practical Use
Use at least several periods for a fair view. One large miss can distort a small sample. Review the row table before trusting the summary. Look for repeated signs. Repeated positive errors suggest persistent bias. Repeated negative errors show the opposite pattern. Compare MFE with MAE and RMSE. MFE can cancel errors, but MAE and RMSE do not. For percentage context, review MAPE when actual values are not zero.
Better Forecast Decisions
After calculating MFE, adjust the forecasting method carefully. Do not change a model from one unusual period. Use recent data, clean outliers, and compare methods. Export the result for review. Keep notes about seasonality, promotions, shortages, and special events. Good forecasting improves when bias is measured often and corrected with discipline. Schedule a monthly review and compare new results against previous baselines. This keeps teams aligned clearly.
FAQs
What is mean forecast error?
Mean forecast error is the average signed difference between actual and forecast values. It shows whether forecasts are biased high or low.
Is MFE the same as MAE?
No. MFE keeps positive and negative signs. MAE uses absolute errors. MFE shows bias, while MAE shows average error size.
Can MFE be negative?
Yes. A negative value means the forecast direction has a negative average error under the selected convention. Read it with the chosen error formula.
Why can MFE be near zero?
Positive and negative errors can cancel each other. A near zero MFE does not always mean each forecast was accurate.
Which error direction should I choose?
Use the direction required by your report. Actual minus forecast is common for demand analysis. Keep the same direction across comparisons.
What does tracking signal mean?
Tracking signal compares total signed error with mean absolute error. Large values can show persistent forecast bias.
Why is MAPE sometimes unavailable?
MAPE needs nonzero actual values. If actual values are zero, percentage error is not valid for those rows.
Can I export my results?
Yes. Use the CSV button for spreadsheet work. Use the PDF button for a report copy.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.