fore.sigplusnoise.wge: Forecasting signal plus noise models

In tswge: Time Series for Data Science

Forecasting signal plus noise models

Description

Forecast models of the form line plus AR noise or cosine plus AR noise with known frequency

Usage

fore.sigplusnoise.wge(x,linear=TRUE,method="mle",freq=0,max.p=5,
n.ahead=10,lastn=FALSE,plot=TRUE,alpha=.05,limits=TRUE)

Arguments

x	The variable containing the realization to be analyzed
linear	If TRUE then the program forecasts a line plus noise model. If FALSE the model is cosine plus noise
method	Estimation method
freq	Frequency of the cosine term. freq is ignored when using line plus noise
max.p	Max value of p for the ARp model fit to the noise
n.ahead	The number of steps ahead to forecast
lastn	If TRUE then the function forecasts the last n.ahead values of the realization. If FALSE the the forecasts are for n.ahead steps beyond the end of the realization
plot	If TRUE then the forecasts and realization are plotted
alpha	Significance level
limits	If TRUE the forecast limits calculated and plotted

Value

f	The n.ahead forecasts
ll	The lower limits for the forecasts. zeros are returned if limits were not requested
ul	The upper limits for the forecasts. zeros are returned if limits were not requested
res	Residuals
wnv	The estimated white noise variance based on the residuals
se	se is the estimated standard error of the k step ahead forecast. zeros are returned if limits were not requested
xi	xi is the kth psi weight associated with the fitted AR model and used to calculate the se above. Note that psi0 is1. zeros are returned if limits were not requested

Author(s)

Wayne Woodward

References

"Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott

Examples

data(llynx)
llynx.for=fore.sigplusnoise.wge(llynx,linear=FALSE,freq=.1,max.p=5,n.ahead=20)

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