This class enables compositional specification of a structural time series model from basic components. Given a list of component models, it represents an additive model, i.e., a model of time series that may be decomposed into a sum of terms corresponding to the component models.
sts_sum(
observed_time_series = NULL,
components,
constant_offset = NULL,
observation_noise_scale_prior = NULL,
name = NULL
)an instance of StructuralTimeSeries.
optional 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 sts_masked_time_series, which includes
a mask tensor to specify timesteps with missing observations.
Default value: NULL.
list of one or more StructuralTimeSeries instances.
These must have unique names.
optional scalar float tensor, or batch of scalars,
specifying a constant value added to the sum of outputs from the
component models. This allows the components to model the shifted series
observed_time_series - constant_offset. If NULL, this is set to the
mean of the provided observed_time_series. Default value: NULL.
optional tfd$Distribution instance
specifying a prior on observation_noise_scale. If NULL, a heuristic
default prior is constructed based on the provided
observed_time_series. Default value: NULL.
string name of this model component; used as name_scope
for ops created by this class. Default value: 'Sum'.
Formally, the additive model represents a random process
g[t] = f1[t] + f2[t] + ... + fN[t] + eps[t], where the f's are the
random processes represented by the components, and
eps[t] ~ Normal(loc=0, scale=observation_noise_scale) is an observation
noise term. See the AdditiveStateSpaceModel documentation for mathematical details.
This model inherits the parameters (with priors) of its components, and
adds an observation_noise_scale parameter governing the level of noise in
the observed time series.
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_sparse_linear_regression()