forecaster.ForecastingModel

Abstract base class for forecasting models.

Usage

Source

forecaster.ForecastingModel()

Subclasses implement model(), which must call predict() exactly once. The instance itself is the (pure) NumPyro model function with signature model_instance(covariates, data=None): the forecast horizon is inferred from the shapes (future = covariates.shape[-2] - data.shape[-2]).

This is the object-oriented façade over the functional API: time_series() and predict() delegate to the free functions in numpyro_forecast.functional, passing the current ~numpyro_forecast.functional.Horizon.

Attributes

Name Description
duration Total horizon length t + future (in time steps).
future Number of forecast time steps f (0 while training).
t_obs Number of observed (in-sample) time steps t.

duration

Total horizon length t + future (in time steps).

duration: int

future

Number of forecast time steps f (0 while training).

future: int

t_obs

Number of observed (in-sample) time steps t.

t_obs: int

Methods

Name Description
__call__() Run the model as a NumPyro model function.
model() Define the generative model and call predict() exactly once.
predict() Register the observation/forecast sites for the model.
time_series() Sample a time-varying latent over the full horizon.

__call__()

Run the model as a NumPyro model function.

Usage

Source

__call__(covariates, data=None)
Parameters
covariates: Array

Covariates with time at axis -2 spanning the full horizon.

data: Array | None = None
Observed data with time at axis -2 (None for prior sampling).

model()

Define the generative model and call predict() exactly once.

Usage

Source

model(zero_data, covariates)
Parameters
zero_data: Array | None

Zeros shaped like the data extended to the covariate duration (shape/dtype only; None during pure prior sampling).

covariates: Array
Covariates with time at axis -2 and shape (*batch, duration, cov).

predict()

Register the observation/forecast sites for the model.

Usage

Source

predict(noise_dist, prediction)

Thin wrapper over numpyro_forecast.functional.predict().

Parameters
noise_dist: dist.Distribution

Zero-centered observation noise (e.g. Normal(0, sigma)).

prediction: Array
Deterministic mean with time at axis -2, shape (*batch, duration, obs).

time_series()

Sample a time-varying latent over the full horizon.

Usage

Source

time_series(name, dist_fn, *, reparam=None)

Thin wrapper over numpyro_forecast.functional.time_series().

Parameters
name: str

Base sample-site name for the in-sample latent.

dist_fn: Callable[[], dist.Distribution]

Zero-argument callable returning the per-step prior distribution.

reparam: Reparam | None = None
Optional reparameterization (e.g. LocScaleReparam) applied to both the in-sample and forecast sites.
Returns
Array
The latent over the full horizon with time at axis -2.