mixle.inference.forecast module

forecast – horizon predictions with honest intervals from a fitted sequence model.

The forecasting front door for state-space families. For a fitted HMM: filter the history to the current state posterior (the forward-backward’s final step), propagate it through the transition matrix, and at each horizon step draw from the exact predictive mixture over states — so the mean, the central interval, and the per-step state probabilities all come from the model itself, for ANY emission family with a sampler (Gaussian, Gamma, categorical, wrapped, neural, …):

f = forecast(hmm, history, horizon=12, level=0.9)
f.mean, f.lo, f.hi          # (H,) arrays (or lists for non-scalar emissions)
f.state_probs               # (H, S): where the chain is expected to be at each step

Sampling-based on purpose: exact for the state marginals (p_T A^h), Monte Carlo only for the emission quantiles, so the intervals are honest for arbitrary (skewed / multimodal / discrete) emission families rather than pretending everything is Gaussian.

class Forecast(mean, lo, hi, level, state_probs, samples=None)[source]

Bases: object

Per-step predictive summaries plus the state-marginal trajectory.

Parameters:
mean: Any
lo: Any
hi: Any
level: float
state_probs: ndarray
samples: Any = None
forecast(model, history, horizon, *, level=0.9, n=4000, seed=0, keep_samples=False)[source]

Forecast horizon steps beyond history under a fitted HMM.

Parameters:

  • model (Any) – a fitted HiddenMarkovModelDistribution (any emission family with a sampler).

  • history (Any) – the observed sequence to condition on (one sequence).

  • horizon (int) – number of future steps to predict.

  • level (float) – central-interval mass (0.9 -> the 5%..95% band).

  • n (int) – Monte-Carlo draws per step for the emission quantiles (state marginals are exact).

  • seed (int) – reproducibility.

  • keep_samples (bool) – also return the raw (H, n) predictive draws (scalar emissions only).

Return type:

Forecast