sts_one_step_predictive {tfprobability}

Compute one-step-ahead predictive distributions for all timesteps

Description

Given samples from the posterior over parameters, return the predictive distribution over observations at each time T, given observations up through time T-1.

Usage

sts_one_step_predictive(
  observed_time_series,
  model,
  parameter_samples,
  timesteps_are_event_shape = TRUE
)

Arguments

observed_time_series

float tensor of shape ⁠concat([sample_shape, model.batch_shape, [num_timesteps, 1]])⁠ where sample_shape corresponds to i.i.d. observations, and the trailing ⁠[1]⁠ dimension may (optionally) be omitted if num_timesteps > 1. May optionally be an instance of sts_masked_time_series, which includes a mask tensor to specify timesteps with missing observations.

model

An instance of StructuralTimeSeries representing a time-series model. This represents a joint distribution over time-series and their parameters with batch shape ⁠[b1, ..., bN]⁠.

parameter_samples

list of tensors representing posterior samples of model parameters, with shapes ⁠list(tf$concat(list(list(num_posterior_draws), param<1>$prior$batch_shape, param<1>$prior$event_shape), list(list(num_posterior_draws), param<2>$prior$batch_shape, param<2>$prior$event_shape), ... ) )⁠ for all model parameters. This may optionally also be a named list mapping parameter names to tensor values.

timesteps_are_event_shape

Deprecated, for backwards compatibility only. If False, the predictive distribution will return per-timestep probabilities Default value: TRUE.

Value

forecast_dist a tfd_mixture_same_family instance with event shape list(num_timesteps) and batch shape tf$concat(list(sample_shape, model$batch_shape)), with num_posterior_draws mixture components. The tth step represents the forecast distribution p(observed_time_series[t] | observed_time_series[0:t-1], parameter_samples).

See Also

Other sts-functions: sts_build_factored_surrogate_posterior(), sts_build_factored_variational_loss(), sts_decompose_by_component(), sts_decompose_forecast_by_component(), sts_fit_with_hmc(), sts_forecast(), sts_sample_uniform_initial_state()