So far, we have looked at ways in which we can explore any linear trends which may be inherent in our data. That provided a solid foundation for the next step, prediction. Now we will begin to look at how we can perform some actual forecasting.
As a preparation step, we will use the ts function to convert our dataframe to a time series object. It is important that the time series be equally spaced before converting to a ts object. At a minimum, you supply the time series variable, and start and end dates as arguments to the ts function.
After creating a new object, x, run a str() function to verify that all of the 14 time series from 1999 to 2012 have been created:
# only extract the 'ALL' timeseries x <- ts(x2$Not.Covered.Pct[1:14], start = c(1999), end = c(2012), frequency = 1) str(x) > Time-Series [1:14] from 1999 to 2012: 0.154 0.157 0.163 0c.161 0.149 ...