mape – Mean absolute percentage error – (100/N) Σ |y_t - ŷ_t| / |y_t|.#

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Operational op under axis point_metrics, sub-layer L5_A_metric_specification, layer l5. Standalone callable: mf.functions.mape.

Function signature#

mf.functions.mape(
    y_true: np.ndarray | pd.Series,
    y_pred: np.ndarray | pd.Series,
    *,
    eps: float = 1e-10,
) -> float

Parameters#

name

type

default

constraint

description

y_true

`np.ndarray

pd.Series`

—

—

y_pred

`np.ndarray

pd.Series`

—

—

eps

float

1e-10

—

Small positive value added to

Returns#

float — scalar result.

Behavior#

Point-forecast metric mape. Scale-free percentage version of MAE. Allows comparing forecasts for targets on different scales (US GDP vs Korean GDP). Pathological when targets can be zero or near-zero – the metric blows up. Hyndman & Koehler (2006) recommend MASE / sMAPE in those cases.

When to use

Cross-target / cross-country comparisons; reporting forecast accuracy in percentage terms.

When NOT to use

Targets that can be near zero (rates, growth rates) – division by tiny |y_t| makes the metric explode.

In recipe context#

Set params.point_metrics = "mape" in the relevant layer to activate this op within a recipe:

# Layer L5 recipe fragment
params:
  point_metrics: mape

References#

  • macroforecast design Part 3, L5: ‘evaluation = (metric × benchmark × aggregation × decomposition × ranking).’

  • Diebold (2017) ‘Forecasting in Economics, Business, Finance and Beyond’, University of Pennsylvania (free online). https://www.sas.upenn.edu/~fdiebold/Textbooks.html

  • Hyndman & Koehler (2006) ‘Another look at measures of forecast accuracy’, International Journal of Forecasting 22(4): 679-688. (doi:10.1016/j.ijforecast.2006.03.001)