Overview

All time intervals must meet the following criteria:

• A discrete time interval has a beginning and an ending SAS date or SAS datetime.
• For SAS date intervals, the ending date is defined as 1 day before the beginning of the next interval.
• For SAS datetime intervals, the ending time is 1 second before the beginning of the next interval.
• All observations with an identifying SAS date or SAS datetime that is between the beginning and the end of the interval t_i correspond to the interval t_i.

SAS Visual Forecasting analyzes the variable assigned to the time variable role to detect the time interval of the data. SAS assumes that all of the values in the time variable are either date or datetime values and distinguishes between the values by their magnitude.

For many businesses, their time seriesan aggregation of transactional data into specified time intervals and sorted according to unique combinations of the default attributes (BY variables) data is equally spaced, or any two consecutive indices have the same difference between the time intervals. The following table shows an equally spaced time series with a one-year interval.

Equally Spaced Time Intervals

Year	Number of Sales
2005	42,100
2006	45,000
2007	47,000
2008	50,000

SAS Visual Forecasting accumulates the data into observations that correspond to the interval that you specify. For nontransactional data, you might need to specify the interval and seasonal cycle length if there are numerous gaps (missing values) in the data. In this case, SAS Visual Forecasting supplies the missing values. A validation routine checks the values of the time variable to determine whether they are spaced according to the interval that you specified.