Time series problems are problems involving a sequence of data points placed in temporal order. We most often represent those data points as a set:
Usually our goal in time series analysis is forecasting; however, there are certainly many other interesting things you can do with a time series that are outside the scope of this book. Forecasting is really just a specialized form of regression, where our goal is to predict some point xt or points 
, given some set of previous points 
. We can do this when the time series is auto correlated, which means the data points are correlated with themselves one or more points back in time (which are called lags). The stronger the auto correlation, the easier it is to forecast.
In many books, time series problems are denoted with y, rather than x, as a hint towards the idea that we typically care to predict a variable y given itself.