Missed TensorFlow Dev Summit? Check out the video playlist. Watch recordings


View source on GitHub

Formal representation of a dynamic linear regresson model.

Inherits From: StructuralTimeSeries

    design_matrix, drift_scale_prior=None, initial_weights_prior=None,
    observed_time_series=None, name=None

The dynamic linear regression model is a special case of a linear Gaussian SSM and a generalization of typical (static) linear regression. The model represents regression weights with a latent state which evolves via a Gaussian random walk:

weights[t] ~ Normal(weights[t-1], drift_scale)

The latent state has dimension num_features, while the parameters drift_scale and observation_noise_scale are each (a batch of) scalars. The batch shape of this Distribution is the broadcast batch shape of these parameters, the initial_state_prior, and the design_matrix. num_features is determined from the last dimension of design_matrix (equivalent to the number of columns in the design matrix in linear regression).


  • design_matrix: float Tensor of shape concat([batch_shape, [num_timesteps, num_features]]).
  • drift_scale_prior: instance of tfd.Distribution specifying a prior on the drift_scale parameter. If None, a heuristic default prior is constructed based on the provided observed_time_series. Default value: None.
  • initial_weights_prior: instance of tfd.MultivariateNormal representing the prior distribution on the latent states (the regression weights). Must have event shape [num_features]. If None, a weakly-informative Normal(0., 10.) prior is used. Default value: None.
  • observed_time_series: float Tensor of shape batch_shape + [T, 1] (omitting the trailing unit dimension is also supported when T > 1), specifying an observed time series. Any priors not explicitly set will be given default values according to the scale of the observed time series (or batch of time series). May optionally be an instance of tfp.sts.MaskedTimeSeries, which includes a mask Tensor to specify timesteps with missing observations. Default value: None.
  • name: Python str for the name of this component. Default value: 'DynamicLinearRegression'.


  • batch_shape: Static batch shape of models represented by this component.

  • design_matrix: Tensor representing the design matrix.

  • initial_state_prior: Prior distribution on the initial latent state (level and scale).

  • latent_size: Python int dimensionality of the latent space in this model.

  • name: Name of this model component.

  • parameters: List of Parameter(name, prior, bijector) namedtuples for this model.



View source


Runtime batch shape of models represented by this component.


  • batch_shape: int Tensor giving the broadcast batch shape of all model parameters. This should match the batch shape of derived state space models, i.e., self.make_state_space_model(...).batch_shape_tensor().


View source


Build the joint density log p(params) + log p(y|params) as a callable.


  • observed_time_series: Observed Tensor trajectories of shape sample_shape + batch_shape + [num_timesteps, 1] (the trailing 1 dimension is optional if num_timesteps > 1), where batch_shape should match self.batch_shape (the broadcast batch shape of all priors on parameters for this structural time series model). May optionally be an instance of tfp.sts.MaskedTimeSeries, which includes a mask Tensor to specify timesteps with missing observations.


  • log_joint_fn: A function taking a Tensor argument for each model parameter, in canonical order, and returning a Tensor log probability of shape batch_shape. Note that, unlike tfp.Distributions log_prob methods, the log_joint sums over the sample_shape from y, so that sample_shape does not appear in the output log_prob. This corresponds to viewing multiple samples in y as iid observations from a single model, which is typically the desired behavior for parameter inference.


View source

    num_timesteps, param_vals=None, initial_state_prior=None, initial_step=0

Instantiate this model as a Distribution over specified num_timesteps.


  • num_timesteps: Python int number of timesteps to model.
  • param_vals: a list of Tensor parameter values in order corresponding to self.parameters, or a dict mapping from parameter names to values.
  • initial_state_prior: an optional Distribution instance overriding the default prior on the model's initial state. This is used in forecasting ("today's prior is yesterday's posterior").
  • initial_step: optional int specifying the initial timestep to model. This is relevant when the model contains time-varying components, e.g., holidays or seasonality.


  • dist: a LinearGaussianStateSpaceModel Distribution object.


View source

    num_timesteps, initial_step=0, params_sample_shape=(),
    trajectories_sample_shape=(), seed=None

Sample from the joint prior over model parameters and trajectories.


  • num_timesteps: Scalar int Tensor number of timesteps to model.
  • initial_step: Optional scalar int Tensor specifying the starting timestep. Default value: 0.
  • params_sample_shape: Number of possible worlds to sample iid from the parameter prior, or more generally, Tensor int shape to fill with iid samples. Default value: .
  • trajectories_sample_shape: For each sampled set of parameters, number of trajectories to sample, or more generally, Tensor int shape to fill with iid samples. Default value: .
  • seed: Python int random seed.


  • trajectories: float Tensor of shape trajectories_sample_shape + params_sample_shape + [num_timesteps, 1] containing all sampled trajectories.
  • param_samples: list of sampled parameter value Tensors, in order corresponding to self.parameters, each of shape params_sample_shape + prior.batch_shape + prior.event_shape.