SKF {dfms}

(Fast) Stationary Kalman Filter

Description

A simple and fast C++ implementation of the Kalman Filter for stationary data with time-invariant system matrices and missing data.

Usage

SKF(X, A, C, Q, R, F_0, P_0, loglik = FALSE)

Arguments

X

numeric data matrix (T \times n).

A

transition matrix (rp \times rp).

C

observation matrix (n \times rp).

Q

state covariance (rp \times rp).

R

observation covariance (n \times n).

F_0

initial state vector (rp \times 1).

P_0

initial state covariance (rp \times rp).

loglik

logical. Compute log-likelihood?

Details

The underlying state space model is:

\textbf{x}_t = \textbf{C} \textbf{F}_t + \textbf{e}_t \sim N(\textbf{0}, \textbf{R})

\textbf{F}_t = \textbf{A F}_{t-1} + \textbf{u}_t \sim N(\textbf{0}, \textbf{Q})

where x_t is X[t, ]. The filter then first performs a time update (prediction)

\textbf{F}_t = \textbf{A F}_{t-1}

\textbf{P}_t = \textbf{A P}_{t-1}\textbf{A}' + \textbf{Q}

where P_t = Cov(F_t). This is followed by the measurement update (filtering)

\textbf{K}_t = \textbf{P}_t \textbf{C}' (\textbf{C P}_t \textbf{C}' + \textbf{R})^{-1}

\textbf{F}_t = \textbf{F}_t + \textbf{K}_t (\textbf{x}_t - \textbf{C F}_t)

\textbf{P}_t = \textbf{P}_t - \textbf{K}_t\textbf{C P}_t

If a row of the data is all missing the measurement update is skipped i.e. the prediction becomes the filtered value. The log-likelihood is computed as

1/2 \sum_t \log(|St|)-e_t'S_te_t-n\log(2\pi)

where S_t = (C P_t C' + R)^{-1} and e_t = x_t - C F_t is the prediction error.

For further details see any textbook on time series such as Shumway & Stoffer (2017), which provide an analogous R implementation in astsa::Kfilter0. For another fast (C-based) implementation that also allows time-varying system matrices and non-stationary data see FKF::fkf.

Value

Predicted and filtered state vectors and covariances.

F

T \times rp filtered state vectors.

P

rp \times rp \times T filtered state covariances.

F_pred

T \times rp predicted state vectors.

P_pred

rp \times rp \times T predicted state covariances.

loglik

value of the log likelihood.

References

Shumway, R. H., & Stoffer, D. S. (2017). Time Series Analysis and Its Applications: With R Examples. Springer.

Harvey, A. C. (1990). Forecasting, structural time series models and the Kalman filter.

Hamilton, J. D. (1994). Time Series Analysis. Princeton university press.

See Also

FIS SKFS

Examples

# See ?SKFS