This example uses the SIMILARITY procedure to illustrate the accumulation of time-stamped transactional data that has been recorded at no particular frequency into time series data at a specific frequency. After the time series is created, the various SAS/ETS procedures related to time series analysis, similarity analysis, seasonal adjustment and decomposition, modeling, and forecasting can be used to further analyze the time series data.
Suppose that the input data set WORK.RETAIL
contains variables STORE
and TIMESTAMP
and numerous other numeric transaction variables. The BY variable STORE
contains values that break up the transactions into groups (BY groups). The time ID variable TIMESTAMP
contains SAS date values recorded at no particular frequency. The other data set variables contain the numeric transaction
values to be analyzed. It is further assumed that the input data set is sorted by the variables STORE
and TIMESTAMP
.
The following statements form monthly time series from the transactional data based on the median value (ACCUMULATE=MEDIAN) of the transactions recorded with each time period. The accumulated time series values for time periods with no transactions are set to zero instead of missing (SETMISS=0). Only transactions recorded between the first day of 1998 (START=’01JAN1998’D ) and last day of 2000 (END=’31JAN2000’D ) are considered and if needed are extended to include this range.
proc similarity data=work.retail out=mseries; by store; id timestamp interval=month accumulate=median setmiss=0 start='01jan1998'd end ='31dec2000'd; target _NUMERIC_; run;
The monthly time series data are stored in the data WORK.MSERIES
. Each BY group associated with the BY variable STORE
contains an observation for each of the 36 months associated with the years 1998, 1999, and 2000. Each observation contains
the variable STORE
, TIMESTAMP
, and each of the analysis variables in the input DATA= data set.
After each set of transactions has been accumulated to form the corresponding time series, the accumulated time series can
be analyzed by using various time series analysis techniques. For example, exponentially weighted moving averages can be used
to smooth each series. The following statements use the EXPAND procedure to smooth the analysis variable named STOREITEM
.
proc expand data=mseries out=smoothed from=month; by store; id timestamp; convert storeitem=smooth / transform=(ewma 0.1); run;
The smoothed series is stored in the data set WORK.SMOOTHED
. The variable SMOOTH
contains the smoothed series.
If the time ID variable TIMESTAMP
contains SAS datetime values instead of SAS date values, the INTERVAL= , START=, and END= options in the SIMILARITY procedure
must be changed accordingly, and the following statements could be used to accumulate the datetime transactions to a monthly
interval:
proc similarity data=work.retail out=tseries; by store; id timestamp interval=dtmonth accumulate=median setmiss=0 start='01jan1998:00:00:00'dt end ='31dec2000:00:00:00'dt; target _NUMERIC_; run;
The monthly time series data are stored in the data WORK.TSERIES
, and the time ID values use a SAS datetime representation.