Cumulative Forecast Error Calculator

Spot forecast bias before it distorts inventory decisions. Review errors, totals, and accuracy metrics instantly. Make confident forecasting decisions using transparent calculations and summaries.

Enter Forecast Series

Paste values separated by commas, spaces, or new lines.

Example: 120 128 133 141 150 158
Use the same number of entries as actual values.
Optional. Leave blank to auto-generate period names.
Common limits are 4 or 4.5 depending on policy.

Example Data Table

Period Actual Forecast Error
Jan1201182
Feb128130-2
Mar1331294
Apr141145-4
May1501473
Jun1581544

Formula Used

Period Error: Errort = Actualt − Forecastt

Cumulative Forecast Error: CFE = Σ(Actualt − Forecastt)

Mean Error: ME = CFE ÷ n

Mean Absolute Deviation: MAD = Σ|Errort| ÷ n

Tracking Signal: TS = CFE ÷ MAD

Root Mean Squared Error: RMSE = √(ΣErrort2 ÷ n)

MAPE: Average of |Errort| ÷ Actualt × 100 for periods where actual values are not zero.

A positive CFE usually indicates underforecasting. A negative CFE usually indicates overforecasting.

How to Use This Calculator

  1. Enter actual observed values in the first field.
  2. Enter matching forecast values in the second field.
  3. Add optional period labels for cleaner reporting.
  4. Select decimal precision and your tracking signal limit.
  5. Click the calculate button to generate bias diagnostics.
  6. Review the summary cards and the detailed period table.
  7. Export the results as CSV or PDF when needed.

FAQs

1. What does cumulative forecast error measure?

It measures the running total of forecast errors across periods. The metric shows whether forecasts systematically drift above or below actual results over time.

2. How is error defined here?

This calculator uses actual minus forecast. Positive values mean actual demand exceeded the forecast. Negative values mean the forecast was higher than actual demand.

3. Why is tracking signal included?

Tracking signal compares cumulative forecast error to mean absolute deviation. It helps reveal persistent bias that might be hidden when you only inspect isolated period errors.

4. What does a positive cumulative forecast error mean?

A positive cumulative forecast error usually means underforecasting. Actual values are collectively higher than forecast values, which can signal stockout risk or unmet demand.

5. What does a negative cumulative forecast error mean?

A negative cumulative forecast error usually means overforecasting. Forecasts are collectively higher than actual values, which can suggest excess inventory or inflated demand expectations.

6. Can I use decimal values?

Yes. The calculator accepts integers and decimals, so it works for units, currency, volumes, energy demand, and many other forecasting datasets.

7. Why does MAPE show N/A sometimes?

MAPE needs non-zero actual values. When actual values are zero, percentage error cannot be computed for those periods, so the calculator excludes them.

8. When should I investigate the forecast process?

Investigate when the tracking signal breaches your chosen control limit, when cumulative error keeps drifting, or when operational decisions are affected by repeated bias.

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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.