|View source on GitHub|
Decompose an observed time series into contributions from each component.
tfp.sts.decompose_by_component( model, observed_time_series, parameter_samples )
Used in the notebooks
|Used in the tutorials|
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.
An instance of
Suppose we've built a model and fit it to data:
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)
To extract the contributions of individual components, pass the time series
and sampled parameters into
component_dists = decompose_by_component( model, observed_time_series=observed_time_series, parameter_samples=samples) # Component mean and stddev have shape `[len(observed_time_series)]`. day_of_week_effect_mean = component_dists[day_of_week].mean() day_of_week_effect_stddev = component_dists[day_of_week].stddev()
Using the component distributions, we can visualize the uncertainty for each component:
from matplotlib import pylab as plt num_components = len(component_dists) xs = np.arange(len(observed_time_series)) fig = plt.figure(figsize=(12, 3 * num_components)) for i, (component, component_dist) in enumerate(component_dists.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)