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