If the ACCUMULATE= option in the ID or VAR statement is specified, data set observations are accumulated within each time period. The frequency (width of each time interval) is specified by the INTERVAL= option in the ID statement. The ID variable contains the time ID values. Each time ID value corresponds to a specific time period. Accumulation is useful when the input data set contains transactional data, whose observations are not spaced with respect to any particular time interval. The accumulated values form the time series, which is used in subsequent analyses.
For example, suppose a data set contains the following observations:
19MAR1999 10 19MAR1999 30 11MAY1999 50 12MAY1999 20 23MAY1999 20
If the INTERVAL=MONTH is specified, all of the preceding observations fall within a three-month period of time between March 1999 and May 1999. The observations are accumulated within each time period as follows:
If the ACCUMULATE=NONE option is specified, an error is generated because the ID variable values are not equally spaced with respect to the specified frequency (MONTH).
If the ACCUMULATE=TOTAL option is specified, the resulting time series is:
O1MAR1999 40 O1APR1999 . O1MAY1999 90
If the ACCUMULATE=AVERAGE option is specified, the resulting time series is:
O1MAR1999 20 O1APR1999 . O1MAY1999 30
If the ACCUMULATE=MINIMUM option is specified, the resulting time series is:
O1MAR1999 10 O1APR1999 . O1MAY1999 20
If the ACCUMULATE=MEDIAN option is specified, the resulting time series is:
O1MAR1999 20 01APR1999 . O1MAY1999 20
If the ACCUMULATE=MAXIMUM option is specified, the resulting time series is:
O1MAR1999 30 O1APR1999 . O1MAY1999 50
If the ACCUMULATE=FIRST option is specified, the resulting time series is:
O1MAR1999 10 O1APR1999 . O1MAY1999 50
If the ACCUMULATE=LAST option is specified, the resulting time series is:
O1MAR1999 30 O1APR1999 . O1MAY1999 20
If the ACCUMULATE=STDDEV option is specified, the resulting time series is:
O1MAR1999 14.14 O1APR1999 . O1MAY1999 17.32
As you can see from the preceding examples, the accumulated time series can have missing values even though the data set observations contain no missing values.