The SIMILARITY procedure computes similarity measures associated with time-stamped data, time series, and other sequentially ordered numeric data. The SIMILARITY procedure includes the following features:
ability to accumulate time-stamped data into a time series
missing value interpretation
zero value interpretation
functional transformations of time series, including the following:
log (LOG)
square-root (SQRT)
logistic (LOGISTIC)
Box-Cox (BOXCOX)
user-defined transformations
simple differencing and seasonal differencing
time series missing value trimming
time warping by compressing or expanding the input sequence with respect to the target sequence
sequence normalizations, including the following:
standard (STANDARD)
absolute (ABSOLUTE)
user-defined normalizations
sequence scaling, including the following:
standard (STANDARD)
absolute (ABSOLUTE)
user-defined scaling
ability to compute similarity measures, including the following:
squared deviation (SQRDEV)
absolute deviation (ABSDEV)
mean square deviation (MSQRDEV)
mean absolute deviation (MABSDEV)
user-defined similarity measures
sliding similarity measures analysis with three types of sequence sliding:
no sliding
slide by time index
slide by season index
support for large data sets