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Forecast Accuracy Calculator

Work out forecast accuracy instantly with clear inputs, formula shown and shareable results.

About Forecast Accuracy Calculator

MAPE expresses error as a percentage of the actual value, which makes it comparable across series of different scales but explodes when actuals approach zero. MAE keeps the original units and treats every error equally, while RMSE squares errors and therefore punishes large misses harder. Bias is the signed average error and reveals systematic over- or under-forecasting that the absolute measures hide.

Formula

Accuracy metrics

MAPE = mean(|forecast - actual| / |actual|); MAE = mean(|error|); RMSE = sqrt(mean(error^2)); bias = sum(error) / sum(actual)

Worked example

With the default values below, Forecast Accuracy Calculator returns MAPE of 4.14%. Change any input above and every figure here is recalculated in your browser.

Inputs used

Actual values
120, 135, 150, 160, 155
Forecast values
115, 140, 145, 170, 150

Results

MAPE
4.14%
Mean absolute error
6.000
Root mean squared error
6.325
Forecast bias
0.00%

Step by step

  1. 1Values used
  2. 2Accuracy metrics
  3. 3MAPE= 4.14
  4. 4Mean absolute error= 6.000
  5. 5Root mean squared error= 6.325
  6. 6Forecast bias= 0.00

Assumptions and limits

  • The result is derived only from the 2 inputs listed above. Anything not among them is not part of the calculation.
  • In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
  • Figures are rounded for display to 3 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

Which metric should I report?

Report bias alongside one magnitude metric. A forecast with low MAPE but consistent positive bias is quietly accumulating excess inventory or capacity.

What can replace MAPE when actuals hit zero?

MASE, which scales error by a naive forecast's error, or weighted MAPE computed on totals rather than per period.

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Forecast Accuracy Calculator | Calcrivo