This model defines a time series given by a sparse linear combination of covariate time series provided in a design matrix:

sts_sparse_linear_regression(
design_matrix,
weights_prior_scale = 0.1,
weights_batch_shape = NULL,
name = NULL
)

design_matrix float tensor of shape tf$concat(list(batch_shape, list(num_timesteps, num_features))). This may also optionally be an instance of tf$linalg$LinearOperator. float Tensor defining the scale of the Horseshoe prior on regression weights. Small values encourage the weights to be sparse. The shape must broadcast with weights_batch_shape. Default value: 0.1. if NULL, defaults to design_matrix.batch_shape_tensor(). Must broadcast with the batch shape of design_matrix. Default value: NULL. the name of this model component. Default value: 'LinearRegression'. Value an instance of StructuralTimeSeries. Details observed_time_series <- tf$matmul(design_matrix, weights)


This is identical to sts_linear_regression, except that sts_sparse_linear_regression uses a parameterization of a Horseshoe prior to encode the assumption that many of the weights are zero, i.e., many of the covariate time series are irrelevant. See the mathematical details section below for further discussion. The prior parameterization used by sts_sparse_linear_regression is more suitable for inference than that obtained by simply passing the equivalent tfd_horseshoe prior to sts_linear_regression; when sparsity is desired, sts_sparse_linear_regression will likely yield better results.

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.

Mathematical Details

The basic horseshoe prior Carvalho et al. (2009) is defined as a Cauchy-normal scale mixture:

scales[i] ~ HalfCauchy(loc=0, scale=1)
weights[i] ~ Normal(loc=0., scale=scales[i] * global_scale)


The Cauchy scale parameters puts substantial mass near zero, encouraging weights to be sparse, but their heavy tails allow weights far from zero to be estimated without excessive shrinkage. The horseshoe can be thought of as a continuous relaxation of a traditional 'spike-and-slab' discrete sparsity prior, in which the latent Cauchy scale mixes between 'spike' (scales[i] ~= 0) and 'slab' (scales[i] >> 0) regimes.

Following the recommendations in Piironen et al. (2017), SparseLinearRegression implements a horseshoe with the following adaptations:

• The Cauchy prior on scales[i] is represented as an InverseGamma-Normal compound.

• The global_scale parameter is integrated out following a Cauchy(0., scale=weights_prior_scale) hyperprior, which is also represented as an InverseGamma-Normal compound.

• All compound distributions are implemented using a non-centered parameterization. The compound, non-centered representation defines the same marginal prior as the original horseshoe (up to integrating out the global scale), but allows samplers to mix more efficiently through the heavy tails; for variational inference, the compound representation implicity expands the representational power of the variational model.

Note that we do not yet implement the regularized ('Finnish') horseshoe, proposed in Piironen et al. (2017) for models with weak likelihoods, because the likelihood in STS models is typically Gaussian, where it's not clear that additional regularization is appropriate. If you need this functionality, please email tfprobability@tensorflow.org.

The full prior parameterization implemented in SparseLinearRegression is as follows:

Sample global_scale from Cauchy(0, scale=weights_prior_scale).
global_scale_variance ~ InverseGamma(alpha=0.5, beta=0.5)
global_scale_noncentered ~ HalfNormal(loc=0, scale=1)
global_scale = (global_scale_noncentered *
sqrt(global_scale_variance) *
weights_prior_scale)
Sample local_scales from Cauchy(0, 1).
local_scale_variances[i] ~ InverseGamma(alpha=0.5, beta=0.5)
local_scales_noncentered[i] ~ HalfNormal(loc=0, scale=1)
local_scales[i] = local_scales_noncentered[i] * sqrt(local_scale_variances[i])
weights[i] ~ Normal(loc=0., scale=local_scales[i] * global_scale)


References

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_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_sum()`