PROC TIMESERIES options ;

The following options can be used in the PROC TIMESERIES statement:

DATA= SAS-data-set

names the SAS data set that contains the input data for the procedure to create the time series. If the DATA= option is not specified, the most recently created SAS data set is used.

CROSSPLOTS= option | ( options )

specifies the cross-variable graphical output desired. By default, the TIMESERIES procedure produces no graphical output. The following plotting options are available:

SERIES

plots the time series (OUT= data set).

CCF

plots the cross-correlation functions (OUTCROSSCORR= data set).

ALL

same as PLOTS=(SERIES CCF).

For example, CROSSPLOTS=SERIES plots the two time series. The CROSSPLOTS= option produces graphical output for these results by using the Output Delivery System (ODS). The CROSSPLOTS= option produces results similar to the data sets listed in parentheses next to the preceding options.

MAXERROR= number

limits the number of warning and error messages that are produced during the execution of the procedure to the specified value. The default is MAXERRORS=50. This option is particularly useful in BY-group processing where it can be used to suppress the recurring messages.

OUT= SAS-data-set

names the output data set to contain the time series variables specified in the subsequent VAR and CROSSVAR statements. If BY variables are specified, they are also included in the OUT= data set. If an ID variable is specified, it is also included in the OUT= data set. The values are accumulated based on the ID statement INTERVAL= or the ACCUMULATE= option or both. The OUT= data set is particularly useful when you want to further analyze, model, or forecast the resulting time series with other SAS/ETS procedures.

OUTCORR= SAS-data-set

names the output data set to contain the univariate time domain statistics.

OUTCROSSCORR= SAS-data-set

names the output data set to contain the cross-correlation statistics.

OUTDECOMP= SAS-data-set

names the output data set to contain the decomposed and/or seasonally adjusted time series.

OUTSEASON= SAS-data-set

names the output data set to contain the seasonal statistics. The statistics are computed for each season as specified by the ID statement INTERVAL= option or the PROC TIMESERIES statement SEASONALITY= option. The OUTSEASON= data set is particularly useful when analyzing transactional data for seasonal variations.

OUTSUM= SAS-data-set

names the output data set to contain the descriptive statistics. The descriptive statistics are based on the accumulated time series when the ACCUMULATE= and/or SETMISSING= options are specified in the ID or VAR statements. The OUTSUM= data set is particularly useful when analyzing large numbers of series and a summary of the results are needed.

OUTTREND= SAS-data-set

names the output data set to contain the trend statistics. The statistics are computed for each time period as specified by the ID statement INTERVAL= option. The OUTTREND= data set is particularly useful when analyzing transactional data for trends.

PLOTS= option | ( options )

specifies the univariate graphical output desired. By default, the TIMESERIES procedure produces no graphical output. The following plotting options are available:

SERIES

plots the time series (OUT= data set).

RESIDUAL

plots the residual time series (OUT= data set).

CYCLES

plots the seasonal cycles (OUT= data set).

CORR

plots the correlation panel (OUTCORR= data set).

ACF

plots the autocorrelation function (OUTCORR= data set).

PACF

plots the partial autocorrelation function (OUTCORR= data set).

IACF

plots the inverse autocorrelation function (OUTCORR= data set).

WN

plots the white noise probabilities (OUTCORR= data set).

DECOMP

plots the seasonal adjustment panel (OUTDECOMP= data set).

TCS

plots the trend-cycle-seasonal component (OUTDECOMP= data set).

TCC

plots the trend-cycle component (OUTDECOMP= data set).

SIC

plots the seasonal-irregular component (OUTDECOMP= data set).

SC

plots the seasonal component (OUTDECOMP= data set).

SA

plots the seasonal adjusted component (OUTDECOMP= data set).

PCSA

plots the percent change in the seasonal adjusted component (OUTDECOMP= data set).

IC

plots the irregular component (OUTDECOMP= data set).

TC

plots the trend component (OUTDECOMP= data set).

CC

plots the cycle component (OUTDECOMP= data set).

ALL

same as PLOTS=(SERIES ACF PACF IACF WN).

For example, PLOTS=SERIES plots the time series. The PLOTS= option produces graphical output for these results by using the Output Delivery System (ODS). The PLOTS= option produces results similar to the data sets listed in parentheses next to the preceding options.

PRINT= option | ( options )

specifies the printed output desired. By default, the TIMESERIES procedure produces no printed output. The following printing options are available:

DECOMP

prints the seasonal decomposition/adjustment table (OUTDECOMP= data set).

SEASONS

prints the seasonal statistics table (OUTSEASON= data set).

DESCSTATS

prints the descriptive statistics for the accumulated time series (OUTSUM= data set).

SUMMARY

prints the descriptive statistics table for all time series (OUTSUM= data set).

TRENDS

prints the trend statistics table (OUTTREND= data set).

For example, PRINT=SEASONS prints the seasonal statistics. The PRINT= option produces printed output for these results by using the Output Delivery System (ODS). The PRINT= option produces results similar to the data sets listed in parentheses next to the preceding options.

PRINTDETAILS

specifies that output requested with the PRINT= option be printed in greater detail.

SEASONALITY= number

specifies the length of the seasonal cycle. For example, SEASONALITY=3 means that every group of three time periods forms a seasonal cycle. By default, the length of the seasonal cycle is one (no seasonality) or the length implied by the INTERVAL= option specified in the ID statement. For example, INTERVAL=MONTH implies that the length of the seasonal cycle is 12.

SORTNAMES

specifies that the variables specified in the VAR and CROSSVAR statements be processed in sorted order by the variable names. This option allows the output data sets to be presorted by the variable names.