You are into the realm of timeseries and forecasting models. You need to fit a model to your current observed data and then forecast the model into the desired time period. I am not sure if the contemporary trend of your observed 1991-2011 will support a back-projection to 1970 but you can certainly try. Just make sure that the resulting distribution in the timeseries makes sense.
The most common model, and the one I would recommend, is an arima(II) mixed-effects model. This is an autoregressive integrated moving averages model that accounts for serial autocorrelation in as the random term. Exponential smoothing are also common.
There are several options in R for specifying time-series models. There is the "arima" function in base with associated functions ("acf", "pacf" and "adf.test") for testing autocorrelation. The "est" function fits an exponential model. There are also many packages; "forecast" and "MAPA" are probably the most useful here. The R Time Series Analysis task view provides a list of relevant packages. The "auto.arima" function will be most helpful because it handles model fitting for you.
There are also base functions for defining time-series objects. Since you are not trying to draw inference from your timeseries there is no need to go into decomposition methods such as spectral analysis and wavelets. However, they are powerful methods that allow you to understand patterns (e.g., seasonality, periodicity) in the timeseries and are worth some attention.
Here is a tutorial on time-series models and forecasting in R.
Here is a very simple worked example using an ARIMA(II) model ("auto.arima" function) in the forecast package.
ts.dat <- AirPassengers
# Autocorrelation function
acf(ts.dat,lag.max=40, main="Autocorrelation Function",ylim=c(-1,1))
# Fit and plot ARIMA model using auto.arima
ts.mdl <- Arima(window(ts.dat, end=1956+11/12), order=c(0,1,1),
seasonal=list(order=c(0,1,1), period=12), lambda=0)
plot(forecast(ts.mdl,h=48), main="ARIMA Forecast")