fore.aruma.wge: Function for forecasting from known model which may have...

In BivinSadler/tswge: Time Series for Data Science

Function for forecasting from known model which may have (1-B)^d, seasonal, and/or other nonstationary factors

Description

This function calculates forecasts from a known model that may have stationary ARMA components as well as (1-B)^d, seasonal, and/or other nonstationary factors

Usage

fore.aruma.wge(x,phi=0,theta=0,d=0,s=0,lambda=0,n.ahead=5,
lastn=FALSE,plot=TRUE,alpha=.05,limits=TRUE)

Arguments

x	Realization to be forecast from
phi	Vector containing stationary AR parameters
theta	Vector containing MA parameters
d	Order of difference
s	Seasonal order
lambda	Vector containing coefficients of nonstationary factors not covered by the difference or the seasonal factors
n.ahead	Number of steps ahead to forecast
lastn	Logical, lastn=TRUE plots forecasts for the last n.ahead values in the realization
plot	Logical, plot=TRUE plots forecasts
alpha	Alpha for prediction limits
limits	Logical, limits=TRUE plots prediction limits

Value

f	Vector of forecasts
ll	Lower limits
ul	Upper limits
resid	Residuals
wnv	White noise variance estimate
xbar	Sample mean of data in x
se	Se for each forecast
psi	Psi weights
ptot.fore	Total order of all AR components, phi, d, s, and lambda
phtot.fore	Coefficients after multiplying all stationary and nonstationary coponents on the AR side of the equation

Author(s)

Wayne Woodward

References

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

Examples

data(airline)
          x=log(airline)
          phi12=c(-.36,-.05,-.14,-.11,.04,.09,-.02,.02,.17,.03,-.1,-.38)
          s=12
          d=1
          fore.aruma.wge(x,phi=phi12,d=1,s=12,n.ahead=12,limits=FALSE)

BivinSadler/tswge documentation built on Sept. 15, 2023, 4:07 p.m.