metrics.eval_pinball()

Mean pinball (quantile) loss of the forecast quantile.

Usage

Source

metrics.eval_pinball(
    pred,
    truth,
    *,
    quantile=0.5,
)

The pinball loss for the forecast \(\hat q\) of quantile \(\tau\) is \(\max(\tau (y - \hat q), (\tau - 1)(y - \hat q))\), averaged over all data elements. At quantile=0.5 it is half the mean absolute error. A pure JAX scalar kernel (see ~numpyro_forecast.typing.Metric); quantile is static so each level specializes its own branch.

Parameters

pred: Float[Array, " sample *batch"]

Forecast samples with the sample axis first.

truth: Float[Array, " *batch"]

Ground-truth values (matching pred without the sample axis).

quantile: float = 0.5

Target quantile in (0, 1).

Returns

Array

The mean pinball loss as a scalar array.

Raises

ValueError

If quantile is not strictly inside (0, 1).