The TIMESERIES Procedure

Example 32.2 Trend and Seasonal Analysis

This example illustrates using the TIMESERIES procedure for trend and seasonal analysis of time-stamped transactional data.

Suppose that the data set SASHELP.AIR contains two variables: DATE and AIR. The variable DATE contains sorted SAS date values recorded at no particular frequency. The variable AIR contains the transaction values to be analyzed.

The following statements accumulate the transactional data on an average basis to form a quarterly time series and perform trend and seasonal analysis on the transactions.

proc timeseries data=sashelp.air
                out=series
                outtrend=trend
                outseason=season print=seasons;
   id date interval=qtr accumulate=avg;
   var air;
run;

The time series is stored in the data set WORK.SERIES, the trend statistics are stored in the data set WORK.TREND, and the seasonal statistics are stored in the data set WORK.SEASON. Additionally, the seasonal statistics are printed (PRINT=SEASONS) and the results of the seasonal analysis are shown in Output 32.2.1.

Output 32.2.1 Seasonal Statistics Table

The TIMESERIES Procedure

Season Statistics for Variable AIR
Season Index	N	Minimum	Maximum	Sum	Mean	Standard Deviation
1	36	112.0000	419.0000	8963.00	248.9722	95.65189
2	36	121.0000	535.0000	10207.00	283.5278	117.61839
3	36	136.0000	622.0000	12058.00	334.9444	143.97935
4	36	104.0000	461.0000	9135.00	253.7500	101.34732

Using the trend statistics stored in the WORK.TREND data set, the following statements plot various trend statistics associated with each time period over time.

title1 "Trend Statistics";
proc sgplot data=trend;
   series x=date y=max  / lineattrs=(pattern=solid);
   series x=date y=mean / lineattrs=(pattern=solid);
   series x=date y=min  / lineattrs=(pattern=solid);
   yaxis display=(nolabel);
   format date year4.;
run;

The results of this trend analysis are shown in Output 32.2.2.

Output 32.2.2 Trend Statistics Plot

Using the trend statistics stored in the WORK.TREND data set, the following statements chart the sum of the transactions associated with each time period for the second season over time.

title1 "Trend Statistics for 2nd Season";
proc sgplot data=trend;
   where _season_ = 2;
   vbar date / freq=sum;
   format date year4.;
   yaxis label='Sum';
run;

The results of this trend analysis are shown in Output 32.2.3.

Output 32.2.3 Trend Statistics Bar Chart

Using the trend statistics stored in the WORK.TREND data set, the following statements plot the mean of the transactions associated with each time period by each year over time.

data trend;
   set trend;
   year = year(date);
run;

title1 "Trend Statistics by Year";
proc sgplot data=trend;
   series x=_season_ y=mean / group=year lineattrs=(pattern=solid);
   xaxis values=(1 to 4 by 1);
run;

The results of this trend analysis are shown in Output 32.2.4.

Output 32.2.4 Trend Statistics

Using the season statistics stored in the WORK.SEASON data set, the following statements plot various season statistics for each season.

title1 "Seasonal Statistics";
proc sgplot data=season;
   series x=_season_ y=max  / lineattrs=(pattern=solid);
   series x=_season_ y=mean / lineattrs=(pattern=solid);
   series x=_season_ y=min  / lineattrs=(pattern=solid);
   yaxis display=(nolabel);
   xaxis values=(1 to 4 by 1);
run;

The results of this seasonal analysis are shown in Output 32.2.5.

Output 32.2.5 Seasonal Statistics Plot