Is something like this what you're looking for?
In the example below, I've tentatively identified an AR(3) model, but I'm sure you can do a thorough analysis of the data to identify the appropriate model. I just want to get the idea across!
Note also that I used the predict() function to generate point forecasts. To create the interval forecasts I used the formula: point forecast $\pm$ 1.28 (standard error). That is, an 80% confidence interval. Change this to 1.96 for a 95% confidence interval.
To create more or fewer forecasts, change the value of n.ahead=12.
# Plot the data
plot(dataframe$LOGINS, type="l")
# Fit an AR(3) model
fit <- arima(x=dataframe$LOGINS, order=c(3,0,0))
# Get point forecasts (12 of them)
forecasts <- predict(fit, n.ahead=12)
# Concatenate LOGINS and the point forecasts (for plotting purposes)
series <- c(dataframe$LOGINS, forecasts$pred)
# Plot LOGINS & the point forecasts
plot(series, type="l")
# Add to the plot the upper 80% forecast C.I.
lines(forecasts$pred+1.28*forecasts$se)
# Add to the plot the lower 80% forecast C.I.
lines(forecasts$pred-1.28*forecasts$se)
You should get something that looks similar to this:
Obviously, you can play around with the plot and make it look the way you want it to.
Let us know if this helps. If it doesn't, tell us why and we'll try to find a solution.
Best Answer
This may be simplistic, but if you have a consistent directory structure on the NOAA site (they usually are), you can recursive wget the entire thing, then sort through it at your leisure.
This will grab everything recursively from that URL and deeper. It's what I normally use when I want a huge whack of data from some (typically government site) and they do silly things like store it in 1-file-per-hour and I need 12 years worth.
As an aside, I presume you only want historical weather data for the United States? If you're working on this on a slightly longer scale and more global, the Berkeley Earth Surface Temperature Project should be releasing their raw data set in the next few months: see here.