Mean Absolute Scaled Error Calculator
Scales forecast absolute error by the in-sample naive forecast error. The worked condition keeps the method and source values visible for an independent check.
Describe the observed sequence in this example
Mean absolute scaled error
The boundary of the claim at the chosen parameters
Construct a second plausible scenario that changes one uncertain input while keeping the rest coherent. Compare the statistic and practical interpretation across both cases. The page-specific quantity is mean absolute scaled error.
If a small defensible change reverses the conclusion, report the sensitivity rather than hiding it behind one preferred scenario. The chosen mean absolute scaled error convention remains attached to the source record. Before reusing this result, write down the observed scale, model boundary, and convention behind the displayed value. That record separates a changed dataset from a changed definition and gives the next analyst a clear route back to the original calculation.
A check on the stated parameter before comparing methods
Scales forecast absolute error by the in-sample naive forecast error. The displayed relationship is MAE forecast / in-sample naive MAE, and each symbol is tied to a labeled field. The page-specific quantity is mean absolute scaled error.
The example produces a MASE of about 0.3333. This example is a numerical check, not proof that the model describes every dataset. The chosen mean absolute scaled error convention remains attached to the source record. Before reusing this result, write down the observed scale, model boundary, and convention behind the displayed value. That record separates a changed dataset from a changed definition and gives the next analyst a clear route back to the original calculation.
For another time-series question, compare autocovariance and mean absolute percentage error.
A compact route to the answer when the result is reused
A MASE below one indicates improvement over the chosen naive benchmark, provided the benchmark denominator is nonzero and comparable. The page-specific quantity is mean absolute scaled error.
The unit of analysis, time order, sample boundary, and treatment of ties or missing values remain outside the answer unless they are entered. Keep those choices beside this time-series result. The chosen mean absolute scaled error convention remains attached to the source record.
A note on convention in the worked condition
Check scales and domains before evaluating mean absolute scaled error. Counts, probabilities, rates, windows, and squared units are not interchangeable merely because a field accepts a number.
If one input changes, predict the direction of the result from the formula first. That catches reversed groups, invalid windows, and parameterization errors. The chosen mean absolute scaled error convention remains attached to the source record.
The quantity this page defines before reporting
Recalculate one intermediate quantity from MAE forecast / in-sample naive MAE and work back from the displayed answer. The source values should be enough for another analyst to reproduce mean absolute scaled error.
Use a boundary case when possible: equal values, a probability near zero, a window of two, or a rate of zero. Expected limiting behavior is often more informative than another decimal place. The chosen mean absolute scaled error convention remains attached to the source record.
Where the method applies under the stated model
The number answers one statistical question. It does not establish causation, model fit, representativeness, or a useful decision threshold by itself. The page-specific quantity is mean absolute scaled error.
Practical meaning depends on the measurement scale and consequences. State the comparison or benchmark before presenting mean absolute scaled error as evidence.
Keeping a reproducible record when the sample changes
A MASE below one indicates improvement over the chosen naive benchmark, provided the benchmark denominator is nonzero and comparable. Outliers, dependence, extrapolation, seasonality, or a mismatched convention can change the appropriate method. The page-specific quantity is mean absolute scaled error.
Choose an alternative because the design or data require it, not because its result is more favorable. Preserve the selected convention in the report. The chosen mean absolute scaled error convention remains attached to the source record.
What the statistic can support during an independent review
Save the entered values, units, formula version, exclusions, and unrounded output with mean absolute scaled error. A copied number without its condition is not reproducible.
Round after downstream calculations are complete. Extra digits cannot repair a biased sample, unstable fit, or unsupported distributional assumption. The chosen mean absolute scaled error convention remains attached to the source record.
Before reporting this result under the stated assumptions
For a second scenario, when should the calculation be repeated?
Repeat it when an input, sample boundary, time window, or model assumption changes. The reported quantity here is mean absolute scaled error.
Before comparing methods, can a missing value be entered as zero?
Only when zero was observed; missingness and a measured zero carry different meanings. The reported quantity here is mean absolute scaled error.
When the result is copied, how many digits should be reported?
Retain guard digits during checking, then round to the resolution supported by the source measurement. The reported quantity here is mean absolute scaled error.
Before interpreting the sign, does this result prove a causal relationship?
No. A robust summary or forecast arithmetic does not replace design, measurement, or substantive reasoning. The reported quantity here is mean absolute scaled error.
Before reporting, what belongs in the saved record?
Preserve the source data, formula convention, units, exclusions, and method version. The reported quantity here is mean absolute scaled error.
Under the stated model, what should be checked before reusing this result?
Keep the inputs, units, method name, exclusions, and unrounded output together. The reported quantity here is mean absolute scaled error.