ABSTRACT
This chapter presents the covariance and correlation functions as basic methods to summarize and visualize stationary time series. The autocovariance function is a tool to express the relation between past and present values of time series and the cross-covariance function is to express the relation between two time series. The chapter considers the case when the mean, the variance and the covariance do not change over time n. If the data are distributed as a normal (Gaussian) distribution, the characteristics of the distribution are completely determined by the mean, the variance and the covariance. However, such an assumption does not hold for many actual data. In such a situation, it is recommended to draw a histogram of the data.
