models.predict()

Register the observation and forecast sites for the model.

Usage

Source

models.predict(
    h,
    obs_dist,
    prediction,
)

prediction is the deterministic predictor over the full horizon (time at axis -2, shape (*batch, duration, obs)). obs_dist takes one of two forms. A numpyro.distributions.Distribution is zero-centered observation noise (e.g. dist.StudentT(nu, 0.0, sigma)) shifted onto the predictor with shift_loc(), which also owns the multivariate-normal layout check. A callable is a link mapping the predictor to the observation distribution directly (the GLM form, e.g. lambda eta: dist.Poisson(jnp.exp(eta))). Either way, while training the observation site "obs" is observed; while forecasting the in-sample prefix is observed and the forecast suffix is sampled at "obs_future" and exposed as the "forecast" deterministic site that forecast() reads. The observation distribution must support time-axis surgery (slice_time() / prefix_condition()), i.e. an elementwise family or a registered one.

Parameters

h: Horizon

The horizon for the current model call (see Horizon).

obs_dist: dist.Distribution | Callable[[Array], dist.Distribution]

Zero-centered noise distribution (shifted onto prediction) or a link callable from the full-horizon predictor to the observation distribution.

prediction: Array

The deterministic predictor over the full horizon, time at axis -2.

Raises

TypeError

If obs_dist is a callable that does not return a distribution (for example a link that returns the predictor itself).

RuntimeError

If forecasting (future > 0) but no observed data is available.

ValueError

If the observation distribution has discrete support but h.data is not integer-dtyped (the usual mistake for count models).