sts_local_level | R Documentation |
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 )
observed_time_series |
optional |
level_scale_prior |
optional |
initial_level_prior |
optional |
name |
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.
an instance of StructuralTimeSeries
.
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()
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