tfprobability: sts_one_step_predictive – R documentation

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sts_one_step_predictive

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)

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.

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).

sts_one_step_predictive

Description

Usage

Arguments

Value

See Also

tfprobability

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