Inference

polars_ts.models.bayesian_ets.inference

Likelihood, parameter packing, log-posterior, MAP, MCMC, and forecasting for Bayesian ETS.

_ses_loglik(values, alpha, level0, sigma)

Gaussian log-likelihood for SES state-space model.

_holt_loglik(values, alpha, beta, level0, trend0, sigma)

Gaussian log-likelihood for Holt's linear trend model.

_hw_loglik(values, alpha, beta, gamma, level0, trend0, seasons0, m, additive, sigma)

Gaussian log-likelihood for Holt-Winters model.

_pack_params(model, alpha, beta, gamma, level0, trend0, seasons0, sigma)

Pack parameters into a flat array for optimization.

_unpack_params(theta, model, m)

Unpack flat parameter array into named parameters.

_log_posterior(theta, values, model, m, additive, priors)

Compute unnormalized log-posterior.

_map_estimate(values, model, m, additive, priors)

Find MAP estimate via L-BFGS-B.

_mcmc_sample(values, model, m, additive, priors, n_samples=1000, burn_in=500, seed=42)

Draw posterior samples via Metropolis-Hastings.

_forecast_from_params(values, params, model, m, additive, h, sigma_noise=False, rng=None)

Run ETS forward to get h-step forecasts from fitted parameters.