ffbs: Forward Filtering Backward Sampling

In astsa: Applied Statistical Time Series Analysis

Forward Filtering Backward Sampling

Description

FFBS algorithm for state space models

Usage

ffbs(y, A, mu0, Sigma0, Phi, sQ, sR, Ups = NULL, Gam = NULL, input = NULL)

Arguments

y	Data matrix, vector or time series.
A	Observation matrix. Can be constant or an array with dim=c(q,p,n) if time varying.
mu0	Initial state mean.
Sigma0	Initial state covariance matrix.
Phi	State transition matrix.
sQ	State error covariance matrix is Q = sQ%*%t(sQ) – see details below. In the univariate case, it is the standard deviation.
sR	Observation error covariance matrix is R = sR%*%t(sR) – see details below. In the univariate case, it is the standard deviation.
Ups	State input matrix.
Gam	Observation input matrix.
input	matrix or vector of inputs having the same row dimension as y.

Details

Refer to Section 6.12 of edition 4 text. For a linear state space model, the FFBS algorithm provides a way to sample a state sequence x_{0:n} from the posterior π(x_{0:n} \mid Θ, y_{1:n}) with parameters Θ and data y_{1:n} as described in Procedure 6.1.

The general model is

x_t = Φ x_{t-1} + Υ u_{t} + sQ\, w_t \quad w_t \sim iid\ N(0,I)

y_t = A_t x_{t-1} + Γ u_{t} + sR\, v_t \quad v_t \sim iid\ N(0,I)

where w_t \perp v_t. Consequently the state noise covariance matrix is Q = sQ\, sQ' and the observation noise covariance matrix is R = sR\, sR' and sQ, sR do not have to be square as long as everything is conformable.

x_t is p-dimensional, y_t is q-dimensional, and u_t is r-dimensional. Note that sQ\, w_t has to be p-dimensional, but w_t does not, and sR\, v_t has to be q-dimensional, but v_t does not.

Value

Xs	An array of sampled states
X0n	The sampled initial state (because R is 1-based)

Note

The script uses Kfilter. If A_t is constant wrt time, it is not necessary to input an array; see the example. The example below is just one pass of the algorithm; see the example at FUN WITH ASTSA for the real fun.

Author(s)

D.S. Stoffer

Source

Shumway & Stoffer (2017) Edition 4, Section 6.12.

References

You can find demonstrations of astsa capabilities at FUN WITH ASTSA.

The most recent version of the package can be found at https://github.com/nickpoison/astsa/.

In addition, the News and ChangeLog files are at https://github.com/nickpoison/astsa/blob/master/NEWS.md.

The webpages for the texts and some help on using R for time series analysis can be found at https://nickpoison.github.io/.

Examples

## Not run: 

## -- this is just one pass --##
# generate some data
 set.seed(1)
 sQ  = 1; sR = 3; n = 100  
 mu0 = 0; Sigma0 = 10; x0 = rnorm(1,mu0,Sigma0)
 w = rnorm(n); v = rnorm(n)
 x = c(x0 + sQ*w[1]);  y = c(x[1] + sR*v[1])   # initialize
for (t in 2:n){
  x[t] = x[t-1] + sQ*w[t]
  y[t] = x[t] + sR*v[t]   
  }

## run one pass of FFBS, plot data, states and sampled states  
run = ffbs(y, A=1, mu0=0, Sigma0=10, Phi=1, sQ=1, sR=3)
tsplot(cbind(y,run$Xs), spaghetti=TRUE, type='o', col=c(8,4), pch=c(1,NA))
legend('topleft', legend=c("y(t)","xs(t)"), lty=1, col=c(8,4), bty="n", pch=c(1,NA))

## End(Not run)

astsa documentation built on Jan. 10, 2023, 1:11 a.m.