The SIMILARITY Procedure
- Overview
- Getting Started
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Syntax
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DetailsAccumulationMissing Value InterpretationZero Value InterpretationTime Series TransformationTime Series DifferencingTime Series Missing Value TrimmingTime Series Descriptive StatisticsInput and Target SequencesSliding SequencesTime WarpingSequence NormalizationSequence ScalingSimilarity MeasuresUser-Defined Functions and SubroutinesOutput Data SetsOUT= Data SetOUTMEASURE= Data SetOUTPATH= Data SetOUTSEQUENCE= Data SetOUTSUM= Data Set_STATUS_ Variable ValuesPrinted OutputODS Table NamesODS Graphics
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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.