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Decompose a forecast distribution into contributions from each component.
tfp.sts.decompose_forecast_by_component( model, forecast_dist, parameter_samples )
model: An instance of
tfp.sts.Sumrepresenting a structural time series model.
Distributioninstance returned by
tfp.sts.forecast(). (specifically, must be a
tfd.LinearGaussianStateSpaceModelparameterized by posterior samples).
Tensorsrepresenting 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
collections.OrderedDictinstance mapping component StructuralTimeSeries instances (elements of
tfd.Distributioninstances representing the marginal forecast for each component. Each distribution has batch and event shape matching
forecast_dist(specifically, the event shape is
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
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)