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
| Time Series Missing Value Trimming |
In some instances, missing values should be interpreted as an unknown observation, but other times, missing values are known and should be interpreted as a zero value. This is the case when missing values are created from accumulation, and a missing observation should be interpreted as having no value (meaning a value of zero). In the former case, the SETMISSING=option in the ID, INPUT, or TARGET, statement can be used to interpret how missing observations should be treated. By default, missing values, at the beginning and ending of the data set, are trimmed from the data set prior to analysis. This can be performed using TRIMMISS=both.