R/sts.R
sts_local_linear_trend_state_space_model.Rd
A state space model (SSM) posits a set of latent (unobserved) variables that
evolve over time with dynamics specified by a probabilistic transition model
p(z[t+1]  z[t])
. At each timestep, we observe a value sampled from an
observation model conditioned on the current state, p(x[t]  z[t])
. The
special case where both the transition and observation models are Gaussians
with mean specified as a linear function of the inputs, is known as a linear
Gaussian state space model and supports tractable exact probabilistic
calculations; see tfd_linear_gaussian_state_space_model
for details.
sts_local_linear_trend_state_space_model( num_timesteps, level_scale, slope_scale, initial_state_prior, observation_noise_scale = 0, initial_step = 0, validate_args = FALSE, allow_nan_stats = TRUE, name = NULL )
num_timesteps  Scalar 

level_scale  Scalar (any additional dimensions are treated as batch
dimensions) 
slope_scale  Scalar (any additional dimensions are treated as batch
dimensions) 
initial_state_prior  instance of 
observation_noise_scale  Scalar (any additional dimensions are
treated as batch dimensions) 
initial_step  Optional scalar 
validate_args 

allow_nan_stats 

name  string prefixed to ops created by this class. Default value: "LocalLinearTrendStateSpaceModel". 
an instance of LinearGaussianStateSpaceModel
.
The local linear trend model is a special case of a linear Gaussian SSM, in
which the latent state posits a level
and slope
, each evolving via a
Gaussian random walk:
level[t] = level[t1] + slope[t1] + Normal(0., level_scale) slope[t] = slope[t1] + Normal(0., slope_scale)
The latent state is the twodimensional tuple [level, slope]
. The
level
is observed at each timestep.
The parameters level_scale
, slope_scale
, and observation_noise_scale
are each (a batch of) scalars. The batch shape of this Distribution
is the
broadcast batch shape of these parameters and of the initial_state_prior
.
Mathematical Details
The linear trend model implements a tfd_linear_gaussian_state_space_model
with latent_size = 2
and observation_size = 1
, following the transition model:
transition_matrix = [[1., 1.] [0., 1.]] transition_noise ~ N(loc = 0, scale = diag([level_scale, slope_scale]))
which implements the evolution of [level, slope]
described above, and the observation model:
observation_matrix = [[1., 0.]] observation_noise ~ N(loc= 0 , scale = observation_noise_scale)
which picks out the first latent component, i.e., the level
, as the
observation at each timestep.
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()
,
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()