The local level model posits a level evolving via a Gaussian random walk:
level[t] = level[t-1] + Normal(0., level_scale)
sts_local_level(
observed_time_series = NULL,
level_scale_prior = NULL,
initial_level_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.
optional tfp$distribution instance specifying a prior
on the level_scale parameter. If NULL, a heuristic default prior is
constructed based on the provided observed_time_series.
Default value: NULL.
optional tfp$distribution instance specifying a
prior on the initial level. If NULL, a heuristic default prior is
constructed based on the provided observed_time_series.
Default value: NULL.
the name of this model component. Default value: 'LocalLevel'.
The latent state is [level]. We observe a noisy realization of the current
level: f[t] = level[t] + Normal(0., observation_noise_scale) at each timestep.
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_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()