This model defines a time series given by a linear combination of covariate time series provided in a design matrix:
observed_time_series <- tf$matmul(design_matrix, weights)
sts_linear_regression(design_matrix, weights_prior = NULL, name = NULL)an instance of StructuralTimeSeries.
float tensor of shape tf$concat(list(batch_shape, list(num_timesteps, num_features))).
This may also optionally be an instance of tf$linalg$LinearOperator.
Distribution representing a prior over the regression
weights. Must have event shape list(num_features) and batch shape
broadcastable to the design matrix's batch_shape. Alternately,
event_shape may be scalar (list()), in which case the prior is
internally broadcast as
tfd_transformed_distribution(weights_prior, tfb_identity(), event_shape = list(num_features), batch_shape = design_matrix$batch_shape).
If NULL, defaults to tfd_student_t(df = 5, loc = 0, scale = 10),
a weakly-informative prior loosely inspired by the
Stan prior choice recommendations.
Default value: NULL.
the name of this model component. Default value: 'LinearRegression'.
The design matrix has shape list(num_timesteps, num_features).
The weights are treated as an unknown random variable of size list(num_features)
(both components also support batch shape), and are integrated over using the same
approximate inference tools as other model parameters, i.e., generally HMC or
variational inference.
This component does not itself include observation noise; it defines a
deterministic distribution with mass at the point
tf$matmul(design_matrix, weights). In practice, it should be combined with
observation noise from another component such as sts_sum, as demonstrated below.
For usage examples see sts_fit_with_hmc(), sts_forecast(), sts_decompose_by_component().
Other sts:
sts_additive_state_space_model(),
sts_autoregressive_state_space_model(),
sts_autoregressive(),
sts_constrained_seasonal_state_space_model(),
sts_dynamic_linear_regression_state_space_model(),
sts_dynamic_linear_regression(),
sts_local_level_state_space_model(),
sts_local_level(),
sts_local_linear_trend_state_space_model(),
sts_local_linear_trend(),
sts_seasonal_state_space_model(),
sts_seasonal(),
sts_semi_local_linear_trend_state_space_model(),
sts_semi_local_linear_trend(),
sts_smooth_seasonal_state_space_model(),
sts_smooth_seasonal(),
sts_sparse_linear_regression(),
sts_sum()