Mean Forecast Error Calculator
Calculates signed average forecast error to reveal directional bias. The form displays mean(actual−forecast) beside mean forecast error, using a worked condition that can be recalculated with the labeled inputs.
Describe the observed sequence for the stated inputs
Mean forecast error
Interpreting the requested mean forecast error
The mean forecast error page calculates signed average forecast error to reveal directional bias.
Mean forecast error is limited to the statistical quantity named by the result panel. The mean forecast error calculation does not silently add a population, time horizon, causal direction, or decision threshold that is absent from the fields.
Inputs that define mean forecast error
- Actual values: For mean forecast error, the displayed actual values sequence is 12, 15, 18, 21, 24, 27. Preserve actual values order when mean forecast error depends on pairing, lag, rank, or time position, and distinguish an observed zero from a missing actual values entry.
- Forecast values: For mean forecast error, the displayed forecast values sequence is 13, 14, 19, 20, 25, 26. Preserve forecast values order when mean forecast error depends on pairing, lag, rank, or time position, and distinguish an observed zero from a missing forecast values entry.
The entries used for mean forecast error must refer to one coherent analysis condition. Combining incompatible populations, periods, or measurement definitions can produce valid mean forecast error arithmetic for a nonexistent study.
From inputs to mean forecast error
For mean forecast error, match every symbol in the relationship to a labeled field before substituting numbers. Mean forecast error is reported in units.
While checking mean forecast error, use actual values observations from one defined analysis set rather than totals copied from incompatible groups.
Verifying the default mean forecast error result
The default mean forecast error condition is Actual values = 12, 15, 18, 21, 24, 27, Forecast values = 13, 14, 19, 20, 25, 26.
The example MFE is 0.00 units.
The live calculator reports Mean forecast error 0. Repeating one intermediate step from mean(actual−forecast) provides a fixed mean forecast error reference check for later code changes.
Conditions attached to mean forecast error
Positive and negative misses can cancel, so MFE should be read beside an absolute error measure.
For mean forecast error, time order is part of the data. For mean forecast error, reordering observations, changing the forecast origin, or mixing incomplete seasonal cycles changes the statistical question.
When mean forecast error can mislead
When interpreting mean forecast error, keep the lag, window, seasonal period, initialization rule, and forecast horizon with the result so a later calculation uses the same timeline.
As a second check for mean forecast error, outliers, ties, ordering, and missing entries can affect mean forecast error even when the number of observations stays unchanged.
A nearby method may answer the next question: root mean squared forecast error and tracking signal.
What to record with mean forecast error
Report mean forecast error using mean(actual−forecast), followed by the entered values, units, exclusions, and analysis date. Name the mean forecast error population or dataset boundary instead of leaving it implicit.
Keep the full calculator output with the record, including Mean forecast error 0. A later mean forecast error review can then distinguish a changed input from a different convention or software implementation.
Questions about mean forecast error
What should be saved with mean forecast error?
Save the entered values and units for actual values, forecast values, along with the analysis date, exclusions, software or formula version, and the relationship mean(actual−forecast). That record is sufficient to rebuild this specific mean forecast error calculation.
Does mean forecast error establish a causal or population conclusion?
No. The displayed mean forecast error value is conditional on the entered data and named method. The mean forecast error design, measurement process, and assumptions determine what can be concluded beyond those values.
How should mean forecast error be rounded?
Keep the unrounded mean forecast error for subsequent arithmetic, then report only the precision supported by the source measurements and the decision context. Extra digits in mean forecast error do not correct sampling or model error.
Which input deserves the closest boundary check?
For mean forecast error, start with forecast values and then actual values. Confirm the mean forecast error units and allowed domain because a valid-looking entry can still describe the wrong statistical setup.
Why could another program report a different mean forecast error?
A different convention for rounding, tails, ties, interpolation, parameterization, or missing values can change mean forecast error. Compare the printed mean forecast error formula and its input definitions before treating either output as wrong.