kalman.wge: Kalman filter for simple signal plus noise model

In tswge: Time Series for Data Science

Kalman filter for simple signal plus noise model

Description

Kalman filter program to predict, filter, and smooth related to the material in Section 10.6 4 in Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Usage

kalman.wge(y, start, gam0, F, gamV, G, gamW)

Arguments

y	the univariate data set to be analyzed
start	the scalar version of Xo in item (c) following the state equation (10.47) of the text
gam0	the scalar version of Gamma(0) discussed in item (c) following the state equation
F	scalar version of the matrix F in the state equation
gamV	the value Gamma(v) specified in item (b) following the state equation
G	the scalar observation matrix specified in the observation equation as G(t)
gamW	the variance of the (univariate) white noise denoted by Gamma(w) in item (c) following (10.48)

Value

pfs	a table giving results such as those in Table 10.1 in Woodward, Gray, and Elliott book

Note

Requires CRAN package 'astsa'

Author(s)

Wayne Woodward

References

Applied Time Series Analysis with R, second edition by Woodward, Gray, and Elliott

Examples

data(table10.1.signal)
data(table10.1.noise)
spn=table10.1.signal+table10.1.noise
kalman.wge(y=spn,start=0,gam0=1,F=.9,gamV=1,G=1,gamW=.75)

tswge documentation built on Feb. 16, 2023, 6:51 p.m.