Mean Absolute Percentage Error Calculator
Calculates average absolute forecast error as a percentage of the actual values. This page keeps mean(|actual−forecast|/|actual|)×100 visible, calculates the worked values immediately, and explains how actual values and forecast values shape the reported mean absolute percentage error.
Define the comparison used by mean absolute percentage error
Current mean absolute percentage error
Understanding the statistical question for Mean Absolute Percentage Error
The page directly calculates average absolute forecast error as a percentage of the actual values; keep that fact with the mean absolute percentage error record.
The requested output is Mean absolute percentage error, not a general verdict about a population or decision, a distinction that matters when relying on mean absolute percentage error. Its numerical meaning comes from mean(|actual−forecast|/|actual|)×100, and its substantive meaning comes from how the source quantities were measured; a second reading of mean absolute percentage error should consider the same point.
Analysts commonly use this calculation when evaluating time-dependent data without discarding sequence, seasonality, or initialization choices; use the same condition when comparing mean absolute percentage error values. The page therefore separates the input labels from the answer and leaves the defining relationship available for review, keeping the mean absolute percentage error workflow transparent.
Tracing the source values for Mean Absolute Percentage Error
The default condition is Actual values = 12, 15, 18, 21, 24, 27; Forecast values = 13, 14, 19, 20, 25, 26; this context belongs beside any decision based on mean absolute percentage error. For mean absolute percentage error, these entries must describe one coherent dataset, study, model, or planning scenario; combining unrelated populations or periods can yield correct arithmetic for an invalid comparison.
- Actual values: The worked entry is 12, 15, 18, 21, 24, 27; it sets one numerical component of mean absolute percentage error through mean(|actual−forecast|/|actual|)×100. For this mean absolute percentage error field, confirm that its population and time boundary match the other entries while following mean(|actual−forecast|/|actual|)×100.
- Forecast values: The worked entry is 13, 14, 19, 20, 25, 26; it anchors one part of mean absolute percentage error through mean(|actual−forecast|/|actual|)×100. For this mean absolute percentage error field, preserve ordering when pairing, rank, lag, or sequence is relevant while following mean(|actual−forecast|/|actual|)×100.
Label each intermediate quantity for mean absolute percentage error by its statistical role instead of relying on its position in the form; this helps separate a data issue from a method issue while auditing mean(|actual−forecast|/|actual|)×100.
Defining the next analysis step for Mean Absolute Percentage Error
Another stage of the workflow may require mean absolute scaled error when that quantity better matches the study question.
A contrasting summary is available in symmetric mean absolute percentage error after confirming that its inputs describe the same observations.
A neighboring analysis is autocovariance without assuming that the two results are interchangeable.
Reviewing the printed relationship for Mean Absolute Percentage Error
mean(|actual−forecast|/|actual|)×100
Read the symbols as a map from the labeled inputs to mean absolute percentage error; make that point explicit in the source record for mean absolute percentage error. In this mean absolute percentage error calculation, preserve parentheses, powers, roots, logarithms, denominators, tail rules, or ordering exactly as printed because changing any of them defines another statistic.
Compare the sign and order of magnitude with what mean(|actual−forecast|/|actual|)×100 predicts before accepting mean absolute percentage error; this preserves the intended interpretation of mean absolute percentage error under mean(|actual−forecast|/|actual|)×100.
Evaluating the worked case for Mean Absolute Percentage Error
The displayed defaults are Actual values = 12, 15, 18, 21, 24, 27; Forecast values = 13, 14, 19, 20, 25, 26; make that point explicit in the source record for mean absolute percentage error.
The example MAPE is approximately 5.5313%.
The live default result is MAPE 5.5313051 %, which is the rule applied here for mean absolute percentage error. When reporting mean absolute percentage error, that fixed case is useful for checking a copied formula, spreadsheet, code revision, or unit convention without inventing a second dataset.
A good manual reconstruction does not need to duplicate every interface step; include that condition when boundary-testing mean absolute percentage error. To reconstruct mean absolute percentage error, recalculate the most informative intermediate quantity in mean(|actual−forecast|/|actual|)×100, then confirm that its direction, sign, and approximate size agree with the displayed mean absolute percentage error.
Reporting the result in context for Mean Absolute Percentage Error
MAPE is undefined for zero actuals and can overweight small denominators; a clear statement of it makes mean absolute percentage error reproducible.
A forecast is conditional on its origin, history, initialization, and horizon rather than a timeless property of the series; a second reading of mean absolute percentage error should consider the same point.
Interpret mean absolute percentage error together with the sample construction, measurement scale, exclusions, and analysis date, keeping the mean absolute percentage error workflow transparent. The evidence behind mean absolute percentage error should support this statement: Another decimal place cannot repair selection bias, incompatible definitions, an inappropriate distribution, or a reversed comparison.
Setting up an independent check for Mean Absolute Percentage Error
For mean absolute percentage error, keep a holdout period separate from model fitting and compare forecast errors at the same horizon and seasonal phase.
Confirm that actual values and forecast values refer to the same analysis condition throughout mean(|actual−forecast|/|actual|)×100; this helps separate a data issue from a method issue while auditing mean(|actual−forecast|/|actual|)×100.
In this mean absolute percentage error calculation, vary actual values while holding the other entries fixed and predict the change before recalculating. Interpret mean absolute percentage error with this condition in view: Then restore the example and vary forecast values; disagreement between the prediction and mean(|actual−forecast|/|actual|)×100 often reveals a transposed field, wrong scale, or mistaken direction.
Working through the method boundary for Mean Absolute Percentage Error
When reporting mean absolute percentage error, the calculator evaluates the quantities supplied to mean(|actual−forecast|/|actual|)×100; it does not verify how observations were collected, whether assumptions were met, or whether mean absolute percentage error is the right endpoint for the decision at hand.
To reconstruct mean absolute percentage error, boundary behavior deserves explicit attention. Check zero denominators, proportions outside their stated scale, impossible counts, insufficient observations, unsupported distribution parameters, and rounded inputs before treating the output as stable; keep that fact with the mean absolute percentage error record.
Carry enough precision through mean(|actual−forecast|/|actual|)×100 to prevent early rounding from moving the reported result; this preserves the intended interpretation of mean absolute percentage error under mean(|actual−forecast|/|actual|)×100.
Making sense of a reporting record for Mean Absolute Percentage Error
A practical mean absolute percentage error check begins with this point: Save the entered values (Actual values = 12, 15, 18, 21, 24, 27; Forecast values = 13, 14, 19, 20, 25, 26), the relationship mean(|actual−forecast|/|actual|)×100, the unrounded calculator output, and the date of analysis. Also retain any exclusions, missing-data treatment, tail choice, confidence level, allocation rule, lag, or parameter convention that affects this particular method, a distinction that matters when relying on mean absolute percentage error.
One safeguard for mean absolute percentage error is straightforward: Report mean absolute percentage error with units or scale where applicable and with enough significant digits for the next calculation. Round the published value only after dependent arithmetic is complete, and label a revised input scenario as a new result rather than overwriting the original record; use the same condition when comparing mean absolute percentage error values.
Compare any software implementation against the exact parameterization printed as mean(|actual−forecast|/|actual|)×100; the result should remain consistent with the structure of mean(|actual−forecast|/|actual|)×100.
Validating scale, direction, and edge cases for Mean Absolute Percentage Error
The evidence behind mean absolute percentage error should support this statement: A magnitude check for mean absolute percentage error starts with the input scale. Counts, proportions, percentages, rates, standardized values, and transformed parameters are not interchangeable even when their bare numbers look similar; this context belongs beside any decision based on mean absolute percentage error.
An audit of mean absolute percentage error turns on a specific detail: Use mean(|actual−forecast|/|actual|)×100 to predict whether increasing actual values should raise, lower, or leave the answer unchanged. A sign reversal or implausible order of magnitude deserves investigation before any narrative interpretation is written; make that point explicit in the source record for mean absolute percentage error.
Interpret mean absolute percentage error with this condition in view: Edge cases for mean absolute percentage error should be chosen from the method rather than at random: examine an allowable boundary, a central case, and a value near a denominator, tail, rank, or support limit when one exists.
Recording the evidence needed for a decision for Mean Absolute Percentage Error
Recalculate mean absolute percentage error from the same premise: Before using mean absolute percentage error in a decision, identify the action it is meant to inform and the consequence of error. The calculator supplies a statistical quantity, while thresholds, costs, benefits, and acceptable uncertainty belong to the surrounding decision process; include that condition when boundary-testing mean absolute percentage error.
Pair the displayed value with the evidence most capable of revealing its weaknesses: raw observations for a summary, counts for a rate, residuals for a fitted model, interval width for an estimate, or alternative assumptions for a design calculation; keep that fact with the mean absolute percentage error record.
If actual values or forecast values comes from an estimate rather than a direct measurement, explain that additional uncertainty instead of presenting mean absolute percentage error as though every input were known exactly, a distinction that matters when relying on mean absolute percentage error.
Questions about the inputs to mean absolute percentage error
What exactly does mean absolute percentage error describe here?
It is the output of mean(|actual−forecast|/|actual|)×100 for the displayed actual values and forecast values; the entered condition does not by itself establish a broader population or causal claim; use the same condition when comparing mean absolute percentage error values.
How can the default mean absolute percentage error example be checked?
Start from Actual values = 12, 15, 18, 21, 24, 27; Forecast values = 13, 14, 19, 20, 25, 26, reproduce one intermediate term in mean(|actual−forecast|/|actual|)×100, and compare with MAPE 5.5313051 %; restore the defaults before testing a second scenario so the records remain distinguishable; this context belongs beside any decision based on mean absolute percentage error.