The SIMILARITY Procedure
- Overview
- Getting Started
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Syntax
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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
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Examples
- References
| Missing Value Interpretation |
Sometimes missing values should be interpreted as unknown values. But sometimes missing values are known, such as when missing values are created from accumulation and no observations should be interpreted as no (zero) value. In the former case, the SETMISSING= option in the ID, INPUT, or TARGET statement can be used to interpret how missing values are treated. The SETMISSING=0 option should be used when missing observations are to be treated as no (zero) values. In other cases, missing values should be interpreted as global values, such as minimum or maximum values of the accumulated series. The accumulated and interpreted time series is used in subsequent analyses.
The SETMISSING=0 option should be used with missing observations are to be treated as a zero value. In other cases, missing values should be interpreted as global values, such as minimum or maximum values of the accumulated series. The accumulated and interpreted time series is then used in subsequent analyses.