Reference

Forecasters

High-level interfaces for fitting and forecasting.

forecaster.Forecaster: Fit a forecasting model with stochastic variational inference.
forecaster.HMCForecaster: Fit a forecasting model with NUTS (Hamiltonian Monte Carlo).

Building forecasting models (object-oriented and functional).

forecaster.ForecastingModel: Abstract base class for forecasting models.
functional.forecasting_model(): Build a NumPyro model from a functional model body.

Pure functional primitives for the train/forecast split.

functional.Horizon: The train/forecast split for a single model call.
functional.time_series(): Sample a time-varying latent over the full horizon.
functional.predict(): Register the observation/forecast sites for the model.
functional.fit_svi(): Fit a forecasting model with stochastic variational inference.
functional.draw_posterior(): Draw num_samples posterior samples of the latent sites from a fit.
functional.fit_mcmc(): Fit a forecasting model with NUTS (Hamiltonian Monte Carlo).
functional.forecast(): Sample forecasts for the steps in [t, duration) from a posterior.
functional.predict_in_sample(): Sample the in-sample posterior predictive of the obs site.
functional.SVIFit: The result of fitting a forecasting model with SVI.
functional.MCMCFit: The result of fitting a forecasting model with MCMC (NUTS).

Rolling-window backtesting and forecast metrics.

evaluate.backtest(): Backtest a forecasting model on a moving window of (train, test) data.
evaluate.BacktestResult: Per-window result of a backtest() run.
evaluate.evaluate_forecast(): Evaluate forecast samples against ground truth for several metrics at once.
evaluate.eval_crps(): Empirical CRPS averaged over all data elements.
evaluate.eval_mae(): Mean absolute error using the forecast sample median as point estimate.
evaluate.eval_rmse(): Root mean squared error using the forecast sample mean as point estimate.
evaluate.eval_coverage(): Empirical coverage of the central alpha prediction interval.
metrics.crps_empirical(): Compute the empirical Continuous Ranked Probability Score (CRPS).

Array helpers and feature builders.

util.fourier_features(): Build a Fourier seasonality design matrix.
util.periodic_repeat(): Tile a seasonal pattern to cover duration time steps.
util.zero_data_like(): Return zeros shaped like data but extended to the covariate duration.
util.concat_future(): Concatenate in-sample and forecast-horizon arrays along the time axis.
util.shift_loc(): Re-center a zero-centered noise distribution at loc.
util.slice_time(): Slice an elementwise distribution along the time axis -2.
util.prefix_condition(): Condition a (t+f)-length distribution on a t-length data prefix.

Example datasets used in the tutorials.

datasets.load_bart_weekly(): Load total weekly BART ridership (log scale) for the univariate example.
datasets.load_bart_hierarchical(): Load the windowed hierarchical BART panel for the hierarchical example.
datasets.load_victoria_electricity(): Load hourly Victoria (Australia) electricity demand and temperature.
datasets.bart_available(): Return whether the BART dataset can be loaded (download succeeds).