relative_mae – Forecast MAE divided by the L4 is_benchmark model’s MAE.#

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

Function signature#

mf.functions.relative_mae(
    y_true: np.ndarray | pd.Series,
    y_model: np.ndarray | pd.Series,
    y_benchmark: np.ndarray | pd.Series,
) -> float

Parameters#

name

type

default

constraint

description

y_true

`np.ndarray

pd.Series`

—

—

y_model

`np.ndarray

pd.Series`

—

—

y_benchmark

`np.ndarray

pd.Series`

—

—

Returns#

float — scalar result.

Behavior#

Relative-loss metric relative_mae. L1-loss analogue of relative_mse. Below 1 means the candidate beats the benchmark on absolute-loss criterion. Robust to heavy-tailed forecast errors.

When to use

Heavy-tailed targets where MSE is too sensitive to outliers.

In recipe context#

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

# Layer L5 recipe fragment
params:
  relative_metrics: relative_mae

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