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tfp.sts.decompose_forecast_by_component

Decompose a forecast distribution into contributions from each component.

tfp.sts.decompose_forecast_by_component(
    model,
    forecast_dist,
    parameter_samples
)

Defined in python/sts/decomposition.py.

Args:

  • model: An instance of tfp.sts.Sum representing a structural time series model.
  • forecast_dist: A Distribution instance returned by tfp.sts.forecast(). (specifically, must be a tfd.MixtureSameFamily over a tfd.LinearGaussianStateSpaceModel parameterized by posterior samples).
  • parameter_samples: Python list of Tensors representing posterior samples of model parameters, with shapes [concat([[num_posterior_draws], param.prior.batch_shape, param.prior.event_shape]) for param in model.parameters]. This may optionally also be a map (Python dict) of parameter names to Tensor values.

Returns:

  • component_forecasts: A collections.OrderedDict instance mapping component StructuralTimeSeries instances (elements of model.components) to tfd.Distribution instances representing the marginal forecast for each component. Each distribution has batch and event shape matching forecast_dist (specifically, the event shape is [num_steps_forecast]).

Examples

Suppose we've built a model, fit it to data, and constructed a forecast distribution:

  day_of_week = tfp.sts.Seasonal(
      num_seasons=7,
      observed_time_series=observed_time_series,
      name='day_of_week')
  local_linear_trend = tfp.sts.LocalLinearTrend(
      observed_time_series=observed_time_series,
      name='local_linear_trend')
  model = tfp.sts.Sum(components=[day_of_week, local_linear_trend],
                      observed_time_series=observed_time_series)

  num_steps_forecast = 50
  samples, kernel_results = tfp.sts.fit_with_hmc(model, observed_time_series)
  forecast_dist = tfp.sts.forecast(model, observed_time_series,
                               parameter_samples=samples,
                               num_steps_forecast=num_steps_forecast)

To extract the forecast for individual components, pass the forecast distribution into decompose_forecast_by_components:

  component_forecasts = decompose_forecast_by_component(
    model, forecast_dist, samples)

  # Component mean and stddev have shape `[num_steps_forecast]`.
  day_of_week_effect_mean = forecast_components[day_of_week].mean()
  day_of_week_effect_stddev = forecast_components[day_of_week].stddev()

Using the component forecasts, we can visualize the uncertainty for each component:

from matplotlib import pylab as plt
num_components = len(component_forecasts)
xs = np.arange(num_steps_forecast)
fig = plt.figure(figsize=(12, 3 * num_components))
for i, (component, component_dist) in enumerate(component_forecasts.items()):

  # If in graph mode, replace `.numpy()` with `.eval()` or `sess.run()`.
  component_mean = component_dist.mean().numpy()
  component_stddev = component_dist.stddev().numpy()

  ax = fig.add_subplot(num_components, 1, 1 + i)
  ax.plot(xs, component_mean, lw=2)
  ax.fill_between(xs,
                  component_mean - 2 * component_stddev,
                  component_mean + 2 * component_stddev,
                  alpha=0.5)
  ax.set_title(component.name)