The statistical foundations of ordinary least squares (OLS) regression are built on independent random variables. But time series observations are seldom, if ever, independent. Observations ordered sequentially in time exhibit time dependence. Time series can also exhibit trends, structural breaks, and repeated patterns that induce seasonality. All of these data features must be accounted for in OLS regression for statistical inference to be meaningful. This chapter discusses the key features of time series data. We explain the difference between stationary and nonstationary data and provide guidelines for how the two types behave differently.