attach(Kopcke)

# First pull off the first 112 points of the IS series, and
# save it as a regular time series object.

IS.new <- rts(IS[1:112])

##Plot the IS series
ts.plot(IS.new)

## Plot the first differences. Do help(diff) to find out about this 
## function
ts.plot(diff(IS.new,1,1))

## Plot second differences
ts.plot(diff(IS.new,1,2))

## Plot the autocorrelation function of IS.new
acf(IS.new)

## Try autocorrelation of the first differences
acf(diff(IS.new,1,1))

## Plot the partial autocorrelation function.
acf(diff(IS.new,1,1),type="partial")

## Define a (2,1,0) ARIMA model.

my.model <- list(list(order=c(2,1,0)))

fit.IS <- arima.mle(IS.new,model=my.model)
print(fit)

## Fit this arima model


## print diagnostics from this model
arima.diag(fit.IS)

## Forecast out 12 periods from this model
arima.forecast(IS.new,model=fit.IS$model,n = 12)