tfp.sts.one_step_predictive

Compute one-step-ahead predictive distributions for all timesteps. (deprecated argument values)

tfp.sts.one_step_predictive(
    model,
    observed_time_series,
    parameter_samples,
    timesteps_are_event_shape=True
)

Used in the notebooks

Used in the tutorials
Structural Time Series Modeling Case Studies: Atmospheric CO2 and Electricity Demand

Deprecated: SOME ARGUMENT VALUES ARE DEPRECATED: (timesteps_are_event_shape=True). They will be removed after 2021-12-31. Instructions for updating: Predictive distributions returned bytfp.sts.one_step_predictivewill soon compute per-timestep probabilities (treating timesteps as part of the batch shape) instead of a single probability for an entire series (the current approach, in which timesteps are treated as event shape). Please update your code to passtimesteps_are_event_shape=False` (this will soon be the default) and to explicitly sum over the per-timestep log probabilities if this is required.

Given samples from the posterior over parameters, return the predictive distribution over observations at each time T, given observations up through time T-1.

Args
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].
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. Any NaNs are interpreted as missing observations; missingness may be also be explicitly specified by passing a tfp.sts.MaskedTimeSeries instance.
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.
timesteps_are_event_shape	Deprecated, for backwards compatibility only. If False, the predictive distribution will return per-timestep probabilities Default value: True.

Returns
predictive_dist	a tfd.MixtureSameFamily instance with event shape [num_timesteps] if timesteps_are_event_shape else [] and batch shape concat([sample_shape, model.batch_shape, [] if timesteps_are_event_shape else [num_timesteps]), with num_posterior_draws mixture components. The tth step represents the forecast distribution p(observed_time_series[t] | observed_time_series[0:t-1], parameter_samples).

Examples

Suppose we've built a model and fit it to data using HMC:

  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)

  samples, kernel_results = tfp.sts.fit_with_hmc(model, observed_time_series)

Passing the posterior samples into one_step_predictive, we construct a one-step-ahead predictive distribution:

  one_step_predictive_dist = tfp.sts.one_step_predictive(
    model, observed_time_series, parameter_samples=samples)

  predictive_means = one_step_predictive_dist.mean()
  predictive_scales = one_step_predictive_dist.stddev()

If using variational inference instead of HMC, we'd construct a forecast using samples from the variational posterior:

  surrogate_posterior = tfp.sts.build_factored_surrogate_posterior(
    model=model)
  loss_curve = tfp.vi.fit_surrogate_posterior(
    target_log_prob_fn=model.joint_distribution(observed_time_series).log_prob,
    surrogate_posterior=surrogate_posterior,
    optimizer=tf.optimizers.Adam(learning_rate=0.1),
    num_steps=200)
  samples = surrogate_posterior.sample(30)

  one_step_predictive_dist = tfp.sts.one_step_predictive(
    model, observed_time_series, parameter_samples=samples)

We can visualize the forecast by plotting:

  from matplotlib import pylab as plt
  def plot_one_step_predictive(observed_time_series,
                               forecast_mean,
                               forecast_scale):
    plt.figure(figsize=(12, 6))
    num_timesteps = forecast_mean.shape[-1]
    c1, c2 = (0.12, 0.47, 0.71), (1.0, 0.5, 0.05)
    plt.plot(observed_time_series, label="observed time series", color=c1)
    plt.plot(forecast_mean, label="one-step prediction", color=c2)
    plt.fill_between(np.arange(num_timesteps),
                     forecast_mean - 2 * forecast_scale,
                     forecast_mean + 2 * forecast_scale,
                     alpha=0.1, color=c2)
    plt.legend()

  plot_one_step_predictive(observed_time_series,
                           forecast_mean=predictive_means,
                           forecast_scale=predictive_scales)

To detect anomalous timesteps, we check whether the observed value at each step is within a 95% predictive interval, i.e., two standard deviations from the mean:

  z_scores = ((observed_time_series[..., 1:] - predictive_means[..., :-1])
               / predictive_scales[..., :-1])
  anomalous_timesteps = tf.boolean_mask(
      tf.range(1, num_timesteps),
      tf.abs(z_scores) > 2.0)