climpred.metrics._mape#

climpred.metrics._mape(forecast: xarray.Dataset, verif: xarray.Dataset, dim: Optional[Union[str, List[str]]] = None, **metric_kwargs: Any) → xarray.Dataset[source]#

Mean Absolute Percentage Error (MAPE).

Mean absolute error (mae) expressed as the fractional error relative to the verification data.

$MAPE = \frac{1}{n} \sum \frac{|f-o|}{|o|}$

Parameters

forecast – Forecast.
verif – Verification data.
dim – Dimension(s) to perform metric over.
metric_kwargs – see xskillscore.mape()

Notes

minimum	0.0
maximum	∞
perfect	0.0
orientation	negative

See also

xskillscore.mape()

Example

>>> HindcastEnsemble.verify(
...     metric="mape", comparison="e2o", alignment="same_verifs", dim="init"
... )
<xarray.Dataset>
Dimensions:  (lead: 10)
Coordinates:
  * lead     (lead) int32 1 2 3 4 5 6 7 8 9 10
    skill    <U11 'initialized'
Data variables:
    SST      (lead) float64 1.536 1.21 1.421 1.149 ... 1.078 1.369 1.833 1.245
Attributes:
    prediction_skill_software:     climpred https://climpred.readthedocs.io/
    skill_calculated_by_function:  HindcastEnsemble.verify()
    number_of_initializations:     64
    number_of_members:             10
    alignment:                     same_verifs
    metric:                        mape
    comparison:                    e2o
    dim:                           init
    reference:                     []

climpred documentation

climpred.metrics._mape

climpred.metrics._mape#