The simple way the CPI and PPI time series are stored in the USPRICE data set in the preceding example is termed the standard form of a time series data set. A time series data set in standard form has the following characteristics:

• The data set contains one variable for each time series.

• The data set contains exactly one observation for each time period.

• The data set contains an ID variable or variables that identify the time period of each observation.

• The data set is sorted by the ID variables associated with date time values, so the observations are in time sequence.

• The data are equally spaced in time. That is, successive observations are a fixed time interval apart, so the data set can be described by a single sampling interval such as hourly, daily, monthly, quarterly, yearly, and so forth. This means that time series with different sampling frequencies are not mixed in the same SAS data set.

Most SAS/ETS procedures that process time series expect the input data set to contain time series in this standard form, and this is the simplest way to store time series in SAS data sets. (The EXPAND and TIMESERIES procedures can be helpful in converting your data to this standard form.) There are more complex ways to represent time series in SAS data sets.

You can incorporate cross-sectional dimensions with BY groups, so that each BY group is like a standard form time series data set. This method is discussed in the section Cross-Sectional Dimensions and BY Groups.

You can interleave time series, with several observations for each time period identified by another ID variable. Interleaved time series data sets are used to store several series in the same SAS variable. Interleaved time series data sets are often used to store series of actual values, predicted values, and residuals, or series of forecast values and confidence limits for the forecasts. This is discussed in the section Interleaved Time Series.