SPECIFIC SPLUS COMMANDS FOR TIME SERIES ANALYSIS

postscript(file="plot.ps",horizontal=F)    ## Plots directed to file "plot.ps"
par(mfrow=c(3,1))

## Basic Time Series Plot, Sample ACF and PACF

yt <- scan("/u/r/e/reinsel/stat349/grind.dat")      ## Read in time series data
ny <- length(yt)
ts.plot(yt, type="b", pch="*", main="Grinding Wheel Profile Data")
yt.acr <- acf(yt, lag.max=25, plot = F)             ## Create ACF for series yt
acf.plot(yt.acr, main="ACF of Original Series yt")
cat("\n ACF for original series yt",fill=T)
print(yt.acr)
yt.pacf <- acf(yt, lag.max=25, type="partial", plot = F)  ## Create PACF for yt
acf.plot(yt.pacf, main="PACF of Original Series yt")
cat("\n PACF for original series yt",fill=T)
print(yt.pacf)

wt <- diff(yt)                                  ## Create 1st differences of yt

## Specifying ARIMA Models, Fitting, Model Checking, and Forecasting

# General form of multiplicative seasonal ARIMA model statement
# model <- list(list(order=c(p,d,q)), list(order=c(P,D,Q), period=S))

par(mfrow=c(1,1))
model1 <- list(order=c(3,0,0))               ## Define ARIMA model to be fitted
yfit1 <- arima.mle(yt, model=model1, xreg=rep(1,ny))   ## Estimate ARIMA model
cat("\n ARIMA(3,0,0) model for original series yt",fill=T)
print(yfit1)
diag1 <- arima.diag(yfit1)               ## Create Diagnostics for fitted model
cat("\n ACF for residuals from ARIMA(3,0,0) model",fill=T)
print(diag1$acf.list)
cat("\n Box-Pierce statistics of residuals from ARIMA(3,0,0) model",fill=T)
print(diag1$gof)
forecast1 <- arima.forecast(yt,model=yfit1$model,n=10,
   xreg=rep(1,(ny+10)),reg.coef=yfit1$reg.coef)   ## Obtain forecasts for model
cat("\n Forecasts of yt for 10 time periods ahead based on ARIMA(3,0,0) model",fill=T)
print(forecast1)

q()                                               ## Ends Splus session