tfp.sts.DynamicLinearRegression

Formal representation of a dynamic linear regresson model.

Inherits From: StructuralTimeSeries

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.

Methods

batch_shape_tensor

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Runtime batch shape of models represented by this component.

Returns
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().

joint_log_prob

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

Args
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.

Returns
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.

make_state_space_model

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Instantiate this model as a Distribution over specified num_timesteps.

Args
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.

Returns
dist a LinearGaussianStateSpaceModel Distribution object.

prior_sample

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Sample from the joint prior over model parameters and trajectories.

Args
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: [] (i.e., draw a single sample and don't expand the shape).
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: [] (i.e., draw a single sample and don't expand the shape).
seed Python int random seed.

Returns
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.