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.

  constant_offset = 0,
  observation_noise_scale = NULL,
  initial_state_prior = NULL,
  initial_step = 0,
  validate_args = FALSE,
  allow_nan_stats = TRUE,
  name = NULL



list containing one or more tfd_linear_gaussian_state_space_model instances. The components will in general implement different time-series models, with possibly different latent_size, but they must have the same dtype, event shape (num_timesteps and observation_size), and their batch shapes must broadcast to a compatible batch shape.#'


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. Default value: 0.#'


Optional scalar float tensor indicating the standard deviation of the observation noise. May contain additional batch dimensions, which must broadcast with the batch shape of elements in component_ssms. If observation_noise_scale is specified for the sts_additive_state_space_model, the observation noise scales of component models are ignored. If NULL, the observation noise scale is derived by summing the noise variances of the component models, i.e., observation_noise_scale = sqrt(sum([ssm.observation_noise_scale**2 for ssm in component_ssms])).


instance of tfd_multivariate_normal representing the prior distribution on latent states. Must have event shape [1] (as tfd_linear_gaussian_state_space_model requires a rank-1 event shape).


Optional scalar integer tensor specifying the starting timestep. Default value: 0.


logical. Whether to validate input with asserts. If validate_args is FALSE, and the inputs are invalid, correct behavior is not guaranteed. Default value: FALSE.


logical. If FALSE, raise an exception if a statistic (e.g. mean/mode/etc...) is undefined for any batch member. If TRUE, batch members with valid parameters leading to undefined statistics will return NaN for this statistic. Default value: TRUE.


string prefixed to ops created by this class. Default value: "AdditiveStateSpaceModel".


an instance of LinearGaussianStateSpaceModel.


The sts_additive_state_space_model represents a sum of component state space models. Each of the N components describes a random process generating a distribution on observed time series x1[t], x2[t], ..., xN[t]. The additive model represents the sum of these processes, y[t] = x1[t] + x2[t] + ... + xN[t] + eps[t], where eps[t] ~ N(0, observation_noise_scale) is an observation noise term.

Mathematical Details

The additive model concatenates the latent states of its component models. The generative process runs each component's dynamics in its own subspace of latent space, and then observes the sum of the observation models from the components.

Formally, the transition model is linear Gaussian:

p(z[t+1] | z[t]) ~ Normal(loc = transition_matrix.matmul(z[t]), cov = transition_cov)

where each z[t] is a latent state vector concatenating the component state vectors, z[t] = [z1[t], z2[t], ..., zN[t]], so it has size latent_size = sum([c.latent_size for c in components]).

The transition matrix is the block-diagonal composition of transition matrices from the component processes:

transition_matrix =
 [[ c0.transition_matrix,  0.,                   ..., 0.                   ],
  [ 0.,                    c1.transition_matrix, ..., 0.                   ],
  [ ...                    ...                   ...                       ],
  [ 0.,                    0.,                   ..., cN.transition_matrix ]]

and the noise covariance is similarly the block-diagonal composition of component noise covariances:

transition_cov =
 [[ c0.transition_cov, 0.,                ..., 0.                ],
  [ 0.,                c1.transition_cov, ..., 0.                ],
  [ ...                ...                     ...               ],
  [ 0.,                0.,                ..., cN.transition_cov ]]

The observation model is also linear Gaussian,

p(y[t] | z[t]) ~ Normal(loc = observation_matrix.matmul(z[t]), stddev = observation_noise_scale)

This implementation assumes scalar observations, so observation_matrix has shape [1, latent_size]. The additive observation matrix simply concatenates the observation matrices from each component:

observation_matrix = concat([c0.obs_matrix, c1.obs_matrix, ..., cN.obs_matrix], axis=-1)

The effect is that each component observation matrix acts on the dimensions of latent state corresponding to that component, and the overall expected observation is the sum of the expected observations from each component.

If observation_noise_scale is not explicitly specified, it is also computed by summing the noise variances of the component processes:

observation_noise_scale = sqrt(sum([c.observation_noise_scale**2 for c in components]))

See also