Example 22.3 Sliding Similarity Analysis

This example illustrates how to compare two time sequences using sliding similarity analysis. The SASHELP.WORKERS data set contains two similar time series variables (ELECTRIC and MASONRY), which represent employment over time. The following statements create an example data set that contains two time series of differing lengths, where the variable MASONRY has the first 12 and last 7 observations set to missing to simulate the lack of data associated with the target series.

   data workers; set sashelp.workers;
      if '01JAN1978'D <= date < '01JAN1982'D then masonry = masonry;
      else masonry = .;
   run;

The goal of sliding similarity measures analysis is find the slide index that corresponds to the most similar subsequence of the input series when compared to the target sequence. The following statements perform sliding similarity analysis on the example data set:

   proc similarity data=workers out=_NULL_ print=(slides summary);
      id date interval=month;
      input  electric;
      target masonry / slide=index measure=msqrdev
                       expand=(localabs=3 globalabs=3)
                       compress=(localabs=3 globalabs=3);
   run;

The DATA=WORKERS option specifies that the input data set WORK.WORKERS is to be used in the analysis. The OUT=_NULL_ option specifies that no output time series data set is to be created. The PRINT=(SLIDES SUMMARY) option specifies that the ODS tables related to the sliding similarity measures and their summary are produced. The INPUT statement specifies that the input variable is ELECTRIC. The TARGET statement specifies that the target variable is MASONRY and that the similarity measure is computed using mean squared deviation (MEASURE=MSQRDEV). The SLIDE=INDEX option specifies observation index sliding. The COMPRESS=(LOCALABS=3 GLOBALABS=3) option limits local and global absolute compression to 3. The EXPAND=(LOCALABS=3 GLOBALABS=3) option limits local and global absolute expansion to 3.