functional.forecast()

Sample forecasts for the steps in [t, duration) from a posterior.

Usage

Source

functional.forecast(
    rng_key, model, posterior, data, covariates, *, batch_size=None
)

Runs Predictive with full-horizon covariates and the in-sample data: the in-sample latent sites are drawn from posterior while the _future suffix is drawn from the prior, and the "forecast" site is returned. The number of forecast samples equals the leading (sample) axis of posterior (see draw_posterior()).

Parameters

rng_key: Array

PRNG key.

model: ForecastModel

The forecasting model callable (the same one that produced posterior).

posterior: dict[str, Array]

Posterior samples of the latent sites, sample axis leading.

data: Array

Observed data with time at axis -2 and length t.

covariates: Array

Covariates with time at axis -2 and length duration > t.

batch_size: int | None = None

Optional chunk size for sampling (caps peak memory).

Returns

Float[Array, " sample *batch future obs"]

Forecast samples over the future = duration - t horizon.

Raises

ValueError

If covariates does not extend beyond data along the time axis.