Using Kalman filters to impute Missing Values in Time Series I am interested in how Kalman Filters can be used to impute missing values in Time Series Data. Is it also applicable if some consecutive time points are missing? I cannot find much on this topic. Any explanations, comments and links are welcome and appreciated!
 A: Preliminaries: Kalman filtering:
Kalman filters operate on state-space models of the form (there are several ways to write it; this is an easy one based on Durbin and Koopman (2012); all of the following is based on that book, which is excellent):
$$
\begin{align}
y_t & = Z \alpha_t + \varepsilon_t \qquad & \varepsilon_t \sim N(0, H) \\
\alpha_{t_1} & = T \alpha_t + \eta_t & \eta_t \sim N(0, Q) \\
\alpha_1 & \sim N(a_1, P_1)
\end{align}
$$
where $y_t$ is the observed series (possibly with missing values) but $\alpha_t$ is fully unobserved. The first equation (the "measurement" equation) says that the observed data is related to the unobserved states in a particular way. The second equation (the "transition" equation) says that the unobserved states evolve over time in a particular way.
The Kalman filter operates to find optimal estimates of $\alpha_t$ ($\alpha_t$ is assumed to be Normal: $\alpha_t \sim N(a_t, P_t)$, so what the Kalman filter actually does is to compute the conditional mean and variance of the distribution for $\alpha_t$ conditional on observations up to time $t$).
In the typical case (when observations are available) the Kalman filter uses the estimate of the current state and the current observation $y_t$ to do the best it can to estimate the next state $\alpha_{t+1}$, as follows:
$$
\begin{align}
a_{t+1} & = T a_t + K_t (y_t - Z \alpha_t) \\
P_{t+1} & = T P_t (T - K_t Z)' + Q
\end{align}
$$
where $K_t$ is the "Kalman gain".
When there is not an observation, the Kalman filter still wants to compute $a_{t+1}$ and $P_{t+1}$ in the best possible way. Since $y_t$ is unavailable, it cannot make use of the measurement equation, but it can still use the transition equation. Thus, when $y_t$ is missing, the Kalman filter instead computes:
$$
\begin{align}
a_{t+1} & = T a_t \\
P_{t+1} & = T P_t T' + Q
\end{align}
$$
Essentially, it says that given $\alpha_t$, my best guess as to $\alpha_{t+1}$ without data is just the evolution specified in the transition equation. This can be performed for any number of time periods with missing data.
If there is data $y_t$, then the first set of filtering equations take the best guess without data, and add a "correction" in, based on how good the previous estimate was.

Imputing data:
Once the Kalman filter has been applied to the entire time range, you have optimal estimates of the states $a_t, P_t$ for $t = 1, 2, \dots, T$. Imputing data is then simple via the measurement equation. In particular, you just calculate:
$$\hat y_t = Z a_t $$

As for a reference, Durbin and Koopman (2012) is excellent; section 4.10 discusses missing observations.


*

*Durbin, J., & Koopman, S. J. (2012). Time series analysis by state
space methods (No. 38). Oxford University Press.

A: The example in the posting that javlacalle points to in their comment features consecutive missing time points. You might also be interested in intervals around the imputed (in-sample forecasted) values, the calculation of which appears in this State Space paper, in section 2.1.
Another paper that might be interesting is this one.
