sts_forecast: Construct predictive distribution over future observations

In tfprobability: Interface to 'TensorFlow Probability'

Construct predictive distribution over future observations

Description

Given samples from the posterior over parameters, return the predictive distribution over future observations for num_steps_forecast timesteps.

Usage

sts_forecast(
  observed_time_series,
  model,
  parameter_samples,
  num_steps_forecast
)

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.
num_steps_forecast	scalar integer tensor number of steps to forecast

Value

forecast_dist a tfd_mixture_same_family instance with event shape list(num_steps_forecast, 1) and batch shape tf$concat(list(sample_shape, model$batch_shape)), with num_posterior_draws mixture components.

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_one_step_predictive(), sts_sample_uniform_initial_state()

Examples

observed_time_series <-
  rep(c(3.5, 4.1, 4.5, 3.9, 2.4, 2.1, 1.2), 5) +
  rep(c(1.1, 1.5, 2.4, 3.1, 4.0), each = 7) %>%
  tensorflow::tf$convert_to_tensor(dtype = tensorflow::tf$float64)
day_of_week <- observed_time_series %>% sts_seasonal(num_seasons = 7)
local_linear_trend <- observed_time_series %>% sts_local_linear_trend()
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]]
preds <- observed_time_series %>%
  sts_forecast(model,
               parameter_samples = samples,
               num_steps_forecast = 50)
predictions <- preds %>% tfd_sample(10)

tfprobability documentation built on Sept. 1, 2022, 5:07 p.m.