This method decomposes a time series according to the posterior represention of a structural time series model. In particular, it:

  • Computes the posterior marginal mean and covariances over the additive model's latent space.

  • Decomposes the latent posterior into the marginal blocks for each model component.

  • Maps the per-component latent posteriors back through each component's observation model, to generate the time series modeled by that component.

sts_decompose_by_component(observed_time_series, model, parameter_samples)



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.


An instance of sts_sum representing a structural time series model.


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.


component_dists A named list mapping component StructuralTimeSeries instances (elements of model$components) to Distribution instances representing the posterior marginal distributions on the process modeled by each component. Each distribution has batch shape matching that of posterior_means/posterior_covs, and event shape of list(num_timesteps).

See also


# \donttest{ observed_time_series <- array(rnorm(2 * 1 * 12), dim = c(2, 1, 12)) day_of_week <- observed_time_series %>% sts_seasonal(num_seasons = 7, name = "seasonal") local_linear_trend <- observed_time_series %>% sts_local_linear_trend(name = "local_linear") model <- observed_time_series %>% sts_sum(components = list(day_of_week, local_linear_trend)) states_and_results <- observed_time_series %>% sts_fit_with_hmc( model, num_results = 10, num_warmup_steps = 5, num_variational_steps = 15 ) samples <- states_and_results[[1]] component_dists <- observed_time_series %>% sts_decompose_by_component(model = model, parameter_samples = samples) # }