To analyze data with the SAS System, data values must be stored in a SAS data set. A SAS data set is a matrix (or table) of data values organized into variables and observations.

The *variables* in a SAS data set label the columns of the data matrix, and the *observations* in a SAS data set are the rows of the data matrix. You can also think of a SAS data set as a kind of file, with the observations
representing records in the file and the variables representing fields in the records. (See
*SAS Language Reference: Concepts* for more information about SAS data sets.)

Usually, each observation represents the measurement of one or more variables for the individual subject or item observed.
Often, the values of some of the variables in the data set are used to identify the individual subjects or items that the
observations measure. These identifying variables are referred to as *ID variables*.

For many kinds of statistical analysis, only relationships among the variables are of interest, and the identity of the observations does not matter. ID variables might not be relevant in such a case.

However, for time series data the identity and order of the observations are crucial. A time series is a set of observations made at a succession of equally spaced points in time.

For example, if the data are monthly sales of a company’s product, the variable measured is sales of the product and the unit observed is the operation of the company during each month. These observations can be identified by year and month. If the data are quarterly gross national product, the variable measured is final goods production and the unit observed is the economy during each quarter. These observations can be identified by year and quarter.

For time series data, the observations are identified and related to each other by their position in time. Since SAS does not assume any particular structure to the observations in a SAS data set, there are some special considerations needed when storing time series in a SAS data set.

The main considerations are how to associate dates with the observations and how to structure the data set so that SAS/ETS procedures and other SAS procedures recognize the observations of the data set as constituting time series. These issues are discussed in following sections.