State-space model (time series, $S_t=F{S}_{t-1}+Z_t,Y_t=H{S}_t+W_t$)

A state-space model separates a latent transition equation $S_t=F{S}_{t-1}+Z_t$ from an observation equation $Y_t=H{S}_t+W_t$. A complete specification includes the initial-state law and its relation to the noise sequences, not just the two equations. Linear second-order models specify covariance structure; Gaussian versions additionally prescribe joint Gaussian laws.

For $|\varphi |<1$, initialize the state by $S_0={\sum }_{j\ge 0}{\varphi }^j{Z}_{-j}$, with variance ${\sigma }_z^2/(1-{\varphi }^2)$. It is orthogonal to future state noise and to observation noise orthogonal to all state noise. Under a Gaussian specification, take the initial state Gaussian with this variance and independent of future noises. Starting at zero instead gives a transient model.