The SIMILARITY Procedure
- Overview
- Getting Started
-
Syntax
-
Details
Accumulation Missing Value Interpretation Zero Value Interpretation Time Series Transformation Time Series Differencing Time Series Missing Value Trimming Time Series Descriptive Statistics Input and Target Sequences Sliding Sequences Time Warping Sequence Normalization Sequence Scaling Similarity Measures User-Defined Functions and Subroutines Output Data Sets OUT= Data Set OUTMEASURE= Data Set OUTPATH= Data Set OUTSEQUENCE= Data Set OUTSUM= Data Set STATUS Variable Values Printed Output ODS Table Names ODS Graphics
-
Examples
- References
| Time Series Differencing |
After optionally transforming the series, the accumulated series can be simply or seasonally differenced using the INPUT or TARGET statement DIF= and SDIF= options. Simple and seasonal differencing are useful when you want to detrend or deseasonalize the time series before computing the similarity measures.
For example, suppose 
is a monthly time series. The following examples of the DIF= and SDIF= options demonstrate how to simply and seasonally difference the time series: DIF=(1,3) specifies first, then third, order differencing; SDIF=(1,3) specifies first, then third, order seasonal differencing.
Additionally, assuming that is strictly positive, the INPUT or TARGET statement TRANSFORM= option and the DIF= and SDIF= options can be combined.