The TIMESERIES Procedure
- Overview
- Getting Started
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Syntax
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DetailsAccumulationMissing Value InterpretationTime Series TransformationTime Series DifferencingDescriptive StatisticsSeasonal DecompositionCorrelation AnalysisCross-Correlation AnalysisSpectral Density AnalysisSingular Spectrum AnalysisData Set OutputOUT= Data SetOUTCORR= Data SetOUTCROSSCORR= Data SetOUTDECOMP= Data SetOUTPROCINFO= Data SetOUTSEASON= Data SetOUTSPECTRA= Data SetOUTSSA= Data SetOUTSUM= Data SetOUTTREND= Data Set_STATUS_ Variable ValuesPrinted OutputODS Table NamesODS Graphics Names
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Examples
- References
Details: TIMESERIES Procedure
- Accumulation
- Missing Value Interpretation
- Time Series Transformation
- Time Series Differencing
- Descriptive Statistics
- Seasonal Decomposition
- Correlation Analysis
- Cross-Correlation Analysis
- Spectral Density Analysis
- Singular Spectrum Analysis
- Data Set Output
- OUT= Data Set
- OUTCORR= Data Set
- OUTCROSSCORR= Data Set
- OUTDECOMP= Data Set
- OUTPROCINFO= Data Set
- OUTSEASON= Data Set
- OUTSPECTRA= Data Set
- OUTSSA= Data Set
- OUTSUM= Data Set
- OUTTREND= Data Set
- _STATUS_ Variable Values
- Printed Output
- ODS Table Names
- ODS Graphics Names
The TIMESERIES procedure can be used to perform trend and seasonal analysis on transactional data. For trend analysis, various sample statistics are computed for each time period defined by the time ID variable and INTERVAL= option. For seasonal analysis, various sample statistics are computed for each season defined by the INTERVAL= or the SEASONALITY= option. For example, if the transactional data ranges from June 1990 to January 2000 and the data are to be accumulated on a monthly basis, then the trend statistics are computed for every month: June 1990, July 1990, …, January 2000. The seasonal statistics are computed for each season: January, February, …, December.
The TIMESERIES procedure can be used to form time series data from transactional data. The accumulated time series can then be analyzed using time series techniques. The data is analyzed in the following order:
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- accumulation
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ACCUMULATE= option in the ID, VAR, or CROSSVAR statement
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- missing value interpretation
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SETMISSING= option in the ID, VAR, or CROSSVAR statement
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- time series transformation
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TRANSFORM= option in the VAR or CROSSVAR statement
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- time series differencing
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DIF= and SDIF= options in the VAR or CROSSVAR statement
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- descriptive statistics
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OUTSUM= option and the PRINT=DESCSTATS option
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- seasonal decomposition
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DECOMP statement or the OUTDECOMP= option in the PROC TIMESERIES statement
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- correlation analysis
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CORR statement or the OUTCORR= option in the PROC TIMESERIES statement
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- singular spectrum analysis
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SSA statement or the OUTSSA= option in the PROC TIMESERIES statement
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- Fourier spectral analysis
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SPECTRA statement or the OUTSPECTRA= option in the PROC TIMESERIES statement
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- cross-correlation analysis
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CROSSCORR statement or the OUTCROSSCORR= option in the PROC TIMESERIES statement