The HPFENGINE Procedure
PROC HPFENGINE Statement
PROC HPFENGINE options;
The PROC HPFENGINE statement invokes the HPFENGINE procedure. You can specify the following options:
- AUXDATA=SAS-data-set
names a SAS data set that contains auxiliary input data for the procedure to use for supplying explanatory variables in a forecast. See the section AUXDATA= Data Set for more information.
- BACK=n
specifies the number of observations before the end of the data where the multistep forecasts are to begin. This option is often used to obtain performance statistics. See the PRINT= option details about printing performance statistics. The default is BACK=0.
- COMPONENTS=INTEGRATE
requests that the component series in the OUTCOMPONENT= data set be of the "integrated" type. The integrated type components might not satisfy the summability constraint. This option affects only the components that are produced by ARIMA models that include differencing. By default, the ARIMA components are generated with the summability constraint. For more information, see "ARIMA Model Based Series Decomposition" in the section ARIMA Models.
- DATA=SAS-data-set
names the SAS data set that contains the input data for the procedure to forecast. If the DATA= option is not specified, the most recently created SAS data set is used.
- ERRORCONTROL=(SEVERITY=(severity-options) STAGE= (stage-options) MAXMESSAGE=number)
-
enables finer control of message printing. The error severity level and HPFENGINE procedure processing stages are set independently. A logical 'and' is taken over all the specified options, and any message that tests true against the results of the 'and' is printed.
Available severity-options are as follows:
- LOW
specifies that only low-severity, minor issues be printed.
- MEDIUM
specifies that only medium-severity problems be printed.
- HIGH
specifies that only severe errors be printed.
- ALL
specifies that errors of all severity levels (LOW, MEDIUM, and HIGH) be printed.
- NONE
specifies that no messages from the HPFENGINE procedure be printed.
Available stage-options are as follows:
- PROCEDURELEVEL
specifies that only errors that occur during option processing and validation be printed.
- DATAPREP
specifies that only errors that occur during the accumulation of data and the application of SETMISS= and ZEROMISS= options be printed.
- SELECTION
specifies that only errors that occur during the model selection process be printed.
- ESTIMATION
specifies that only errors that occur during the model parameter estimation process be printed.
- FORECASTING
specifies that only errors that occur during the model evaluation and forecasting process be printed.
- ALL
is the same as specifying all PROCEDURELEVEL, DATAPREP, SELECTION, ESTIMATION, and FORECASTING options.
Examples are as follows:
errorcontrol=(severity=(high medium) stage=all);prints high- and moderate-severity errors at any processing stage of PROC HPFENGINE.
errorcontrol=(severity=high stage=dataprep);prints high-severity errors only during the data preparation.
errorcontrol=(severity=none stage=all);turns off messages from PROC HPFENGINE.
errorcontrol=(severity=(high medium low) stage=(procedurelevel dataprep selection estimation forecasting));specifies the default behavior. Also the following statement specifies the default behavior:
errorcontrol=(severity=all stage=all) - EXCEPTIONS=except-option
-
specifies the desired handling of arithmetic exceptions during the run. You can specify except-option as one of the following:
- IGNORE
specifies that PROC HPFENGINE stop on an arithmetic exception. No recovery is attempted. This is the default behavior if the EXCEPTIONS= option is not specified.
- CATCH(ESM)
specifies that PROC HPFENGINE generate a forecast based on using its default exponential smoothing model for the variable that produces the arithmetic exception in the current BY group. The ESM model is equivalent to the default model used by the HPFESMSPEC procedure with no modifications.
- CATCH(RW)
specifies that PROC HPFENGINE generate a forecast based on a zero-drift random walk model for the variable that produces the exception in the current BY group.
- CATCH(MISSING)
specifies that PROC HPFENGINE generate a forecast of missing values for the variable that produces the exception in the current BY group.
The order of CATCH handling corresponds to the order of the preceding list. If CATCH(ESM) handling produces an arithmetic exception, it attempts to generate a forecast by using CATCH(RW) semantics. Likewise, if CATCH(RW) handling produces an arithmetic exception, it generates a missing value forecast.
- FORCEBACK
specifies alternate behavior for hold back when there is insufficient data to fit any model and honor the requested hold back sample size. The default behavior dynamically resets to no hold back samples for the current BY group if excluding the requested number of hold back samples leaves insufficient non-missing observations to fit any model. This allows PROC HPFENGINE to make an absolute effort to generate a statistical forecast for each BY group regardless of the condition of the input data set. Sometimes it can be desirable to sacrifice this behavior in order to preserve a consistent time base across the BY groups in the OUT= and OUTFOR= data sets. This is the purpose of FORCEBACK. It alters the default behavior of PROC HPFENGINE to ensure that a consistent hold back sample size is used across all BY groups of the input data set. If a series has insufficient observations to permit a model fit after excluding hold back samples, a missing value forecast is generated over the range of time ID’s corresponding to the complete forecast horizon including the hold back region. Additionally, for both OUTSUM= and OUTEST= data sets, the _STATUS_ variable is set to indicate the excepted forecast produced by the presence of FORCEBACK.
- GLOBALSELECTION=catalog-name
specifies the name of a catalog entry that serves as a model selection list. This is the selection list used to forecast all series if no INEST= data set is provided. It is also the selection list used if individual model selections are missing in the INEST= data set when INEST= is provided. If REPOSITORY= is not present, GLOBALSELECTION defaults to BEST, specified in SASHELP.HPFDFLT.
- IGNORECHOOSE
specifies that the CHOOSE= option in the HPFSELECT procedure be ignored when selecting a model in the candidate model list. The best model is selected regardless of the model chosen in the CHOOSE= option in the HPFSELECT procedure.
- INEST=SAS-data-set
contains information that maps forecast variables to models or selection lists, and data set variables to model variables. It can also contain parameter estimates used if the TASK=FORECAST or TASK=UPDATE options are present. INEST= is optional. See the description of the GLOBALSELECTION= option for more information.
- INEVENT=SAS-data-set
contains information that describes predefined events. This data set is usually created by the HPFEVENTS procedure. This option is only used if events are included in a model.
- LEAD=n
-
specifies the number of periods ahead to forecast (forecast lead or horizon). The default is LEAD=12.
The LEAD= value is relative to the last observation in the input data set and not to the last nonmissing observation of a particular series. Thus, if a series has missing values at the end, the actual number of forecasts computed for that series will be greater than the LEAD= value.
- OUT=SAS-data-set
names the output data set to contain the forecasts of the variables specified in the subsequent FORECAST statements. If an ID variable is specified, it will also be included in the OUT= data set. The values are accumulated based on the ACCUMULATE= option and forecasts are appended to these values. If the OUT= data set is not specified, a default output data set DATAn is created. If you do not want the OUT= data set created, then use OUT=_NULL_.
- OUTACCDATA=SAS-data-set
names the output data set to store accumulation mode used for each forecast variable specified for the HPFENGINE procedure. This data set is commonly used to transfer information to the HPFTEMPRECON procedure so it can properly affect constraint formulation for the benchmarking process.
- OUTCOMPONENT=SAS-data-set
names the output data set to contain the forecast components. The components included in the output depend on the model.
- OUTEST=SAS-data-set
-
contains information that maps forecast variables to model specifications, and data set variables to model variables and parameter estimates.
An OUTEST= data set will frequently be used as the INEST= data set for subsequent invocations of PROC HPFENGINE. In such a case, if the PROC HPFENGINE statement option TASK=FORECAST is used, forecasts are generated using the parameter estimates found in this data set and are not reestimated.
- OUTFOR=SAS-data-set
names the output data set to contain the forecast time series components (actual, predicted, lower confidence limit, upper confidence limit, prediction error, and prediction standard error). The OUTFOR= data set is particularly useful for displaying the forecasts in tabular or graphical form.
- OUTINDEP=SAS-data-set
names the output data set to contain input in the forecasting process. This information is useful if future values of input variables are automatically supplied by the HPFENGINE procedure. Such a case would occur if one or more input variables are listed in either the STOCHASTIC or the CONTROLLABLE statement and if there are missing future values of these input variables.
- OUTMODELINFO=SAS-data-set
names the output data set to contain detailed information about the selected forecast model. The data set has information such as the model family, presence or absence of inputs, events and outliers, and so forth.
- OUTPROCINFO=SAS-data-set
names the output data set to contain information in the SAS log, specifically the number of notes, errors, and warnings and the number of series processed, forecasts requested, and forecasts failed.
- OUTSTAT=SAS-data-set
names the output data set to contain the statistics of fit (or goodness-of-fit statistics). The OUTSTAT= data set is useful for evaluating how well the model fits the series. The statistics of fit are based on the entire range of the time series.
- OUTSTATSELECT=SAS-data-set
names the output data set to contain statistics of fit for all of the candidate models fit during model selection. The OUTSTATSELECT= data set is useful for comparing the performance of various models.
- OUTSUM=SAS-data-set
names the output data set to contain the summary statistics and the forecast summation. The summary statistics are based on the accumulated time series for the dependent variables acquired from the DATA= data set. The forecast summations are based on the LEAD= option setting. The OUTSUM= data set is particularly useful when forecasting large numbers of series and a summary of the results is needed.
-
PLOT <(global-plot-options)> =plot-request <(options)>
PLOT <(global-plot-options)> =(plot-request <(options)> <…plot-request <(options)>>) -
specifies the graphical output that you want. By default, the HPFENGINE procedure produces no graphical output.
Any specified global-plot-options are available to all of the requested plots. They are interpreted by the individual plots that are requested, so it is possible to specify global-plot-options that have no effect for a particular set of plot requests. Each of the following global-plot-options description includes the set of plots for which it has some effect.
You can specify the following global-plot-options:
- NLAGS=number
-
specifies the number of lags to include in various error series plots. This option affects the ACF, IACF, PACF, and WN plots. The default number of lags is given by
In all cases,
is automatically limited to half of the number of observations in the error series.
You can specify the following plot-requests:
- ACF
plots the prediction error autocorrelation function.
- ALL
is equivalent to specifying PLOT=(ACF COMPONENTS ERRORS FORECASTCYCLES FORECASTS FORECASTSONLY IACF MODELFORECASTS MODELS PERIODOGRAM SPECTRUM WN).
- BASIC
is equivalent to specifying PLOT=(CORR ERRORS MODELFORECASTS). In the context of IDM models, the stocking level plot is generated in place of the prediction error correlation panel plot.
- CANDIDATES
plots model (forecast) and error series for each candidate model that is fit to the dependent series.
- COMPONENTS
plots the forecast components.
- CORR
plots the prediction error series graphics panel that contains the ACF, IACF, PACF, and white noise probability plots. In the context of IDM models, PLOT=CORR produces no additional output.
- ERRORS=<(SMOOTH=method)>
-
plots prediction error time series. You can specify the following method values:
- NONE
specifies that no smoothed error series overlay be applied to the plot.
- LOESS
specifies that a LOESS fit be used to generate a smoothed error series overlay for the plot.
PLOT=ERRORS is equivalent to PLOT=ERRORS(SMOOTH=LOESS).
- FORECASTCYCLES
plots the forecast seasonal cycles.
- FORECASTS
plots the actual time series and its one-step-ahead forecast over the historical period; plots the forecast and its confidence bands over the forecast horizon.
- FORECASTSONLY
plots the forecast and its confidence bands over the forecast horizon only.
- IACF
plots the prediction error inverse autocorrelation function.
- MODELFORECASTS
plots the one-step-ahead model forecast and its confidence bands in the historical period; plots the forecast and its confidence bands over the forecast horizon.
- MODELS
plots the one-step-ahead model forecast and its confidence bands in the historical period.
- PACF
plots the prediction error partial autocorrelation function.
- PERIODOGRAM
plots periodogram of the prediction error series.
- SPECTRUM=<(spectrum-options)>
-
plots the periodogram and the smoothed periodogram of the prediction error series in a single graph.
You can specify the following spectrum-options:
- ALPHA=value
specifies the significance level for upper and lower confidence limits around the smoothed periodogram estimates of spectral density. By default, ALPHA=0.4.
- CENTER=YES | NO
specifies whether mean adjustment is desired for the error series before computation of the smoothed periodogram estimates of spectral density. By default, CENTER=NO.
PLOT=SPECTRUM is equivalent to PLOT=SPECTRUM(ALPHA=0.4 CENTER=NO).
- WN
plots white noise graphics.
For example, PLOT=FORECASTS plots the forecasts for each series; PLOT(NLAGS=12)=(ACF IACF) produces the prediction error autocorrelation and inverse autocorrelation plots for which the number of lags restricted to 12.
- PRINT=option | (options )
-
specifies printed output. By default, the HPFENGINE procedure produces no printed output.
The following printing options are available:
- ALL
is the same as specifying PRINT=(ESTIMATES SELECT FORECASTS STATISTICS BIAS DESCSTATS). PRINT=(ALL CANDIDATES COMPONENTS PERFORMANCE PERFORMANCESUMMARY PERFORMANCEOVERALL) prints all the options listed.
- BIAS
prints model bias information.
- CANDIDATES
prints parameter estimates for each candidate model evaluated during the model selection step.
- COMPONENTS
prints forecast model components.
- DESCSTATS
prints descriptive statistics the series forecast series.
- ESTIMATES
prints parameter estimates for the selected model.
- FORECASTS
prints the forecasts.
- PERFORMANCE
prints the performance statistics for each forecast.
- PERFORMANCESUMMARY
prints the performance summary for each BY group.
- PERFORMANCEOVERALL
prints the performance summary for all of the BY groups.
- SELECT
prints the label and fit statistics for each model in the selection list.
- STATISTICS
prints the statistics of fit.
- SUMMARY
prints the forecast summary.
- PRINTDETAILS
specifies that output requested with the PRINT= option be printed in greater detail.
- REPOSITORY=catalog-name
is a two-level SAS catalog name that specifies the location of the model repository. The REPOSITORY= option can also be specified as MODELREPOSITORY=, MODELREP=, or REP=. The default for this option is Sashelp.Hpfdflt.
- SCOREREPOSITORY=catalog-name
is a two-level SAS catalog name that specifies the location of the model score repository. Score files are written to this repository if the SCORE statement is used in the HPFENGINE procedure. There is no default score repository. The presence of a SCORE statement requires that the SCOREREPOSITORY= option also be present.
- SEASONALITY=n
specifies the length of the seasonal cycle. For example, SEASONALITY=3 means that every group of three observations forms a seasonal cycle. The SEASONALITY= option is applicable only for seasonal forecasting models. By default, the length of the seasonal cycle is 1 (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 FORECAST statement be processed in sorted order.
- STAT=ADJUST | NOADJUST
-
specifies whether the statistics of fit be computed before or after the forecast dependent series is post-adjusted.
- ADJUST
specifies that statistics of fit be computed after the forecast series is post-adjusted. This is the default.
- NOADJUST
specifies that statistics of fit be computed before the forecast series is post-adjusted.
- TASK=action(override-options)
-
controls the model selection and parameter estimation process. The following action values are valid. All override-options are admissible for any action. See section TASK= Option Details for details.
- SELECT
performs model selection, estimates parameters of the selected model, and produces forecasts. This is the default.
- SELECT(override-options)
performs model selection, estimates parameters of the selected model, produces forecasts, and potentially overrides settings in the model selection list. If a selection list does not specify a particular item and that item is specified with a TASK=SELECT option, the value as set in TASK=SELECT is used. If an option is specified in selection list, the corresponding value set in TASK=SELECT is not used unless the OVERRIDE option is also present. See section TASK= Option Details for possible values of override-options and other details.
- FIT
estimates parameters by using the model specified in the INEST= data set, and then forecasts. No model selection is performed. New parameter estimates are saved if an OUTEST= data set is specified. See section TASK= Option Details for details.
- FIT(override-options)
performs the same action as TASK=FIT supplemented by any override-options specified. See section TASK= Option Details for details.
- UPDATE
estimates parameters by using the model specified in the INEST= data set, and then forecasts. TASK=UPDATE differs from TASK=FIT in that the parameters found in the INEST= data set are used as starting values in the estimation. No model selection is performed. New parameter estimates are saved if an OUTEST= data set is specified. See section TASK= Option Details for details.
- UPDATE(override-options)
performs the same action as TASK=UPDATE supplemented by any override-options specified. See section TASK= Option Details for details.
- FORECAST
forecasts using model and parameters specified in the INEST= data set. No parameter estimation nor model selection occurs. See section TASK= Option Details for details.
- FORECAST(override-options)
performs the same action as TASK=FORECAST supplemented by any override-options specified. See section TASK= Option Details for details.
TASK= Option Details
This section describes the override-options that can be specified in any TASK=action context. Many of these settings are used only during the model selection phase of PROC HPFENGINE processing (that is, during the processing performed when you specify TASK=SELECT). However, there can be a need for them in any TASK=action context. PROC HPFENGINE can generate special placeholder forecasts for some of the time series it encounters during a TASK=SELECT run. These are generally the result of nominal data that prohibits PROC HPFENGINE from performing an evaluation of the model selection list for the series. As such, these forecasts represent excepted cases. Many of these cases are triggered from various override-options in effect during the TASK=SELECT run. For example, if you specified TASK=SELECT(MINOBS=6), any series with fewer than six nonmissing observations would trigger a forecast using the _MEAN_ model. Another example would be the _ZERO_ model that arises from use of the ENDZEROS= option. PROC HPFENGINE recognizes these excepted cases when processing the information from its INEST= data set that is saved as the OUTEST= data set from a previous PROC HPFENGINE run. When recognized in this way, PROC HPFENGINE attempts to perform model selection processing on those excepted cases regardless of the TASK=action requested. To deal with any such cases properly and ensure consistent behavior, you should specify the same set of override-options for your PROC HPFENGINE runs without regard to the action value specified.
You can specify these override-options in the context of any admissible action for TASK=.
- ALPHA=number
-
specifies the significance level to use in computing the confidence limits of the forecast. The ALPHA= value must be between 0 and 1. For example, ALPHA=0.05 produces 95% confidence intervals.
If a selection list does not specify ALPHA and ALPHA is specified in TASK=, the value that is specified takes effect. If ALPHA is specified in the selection list, the corresponding value specified in TASK= is not used unless the OVERRIDE option is also present.
- CRITERION=option
-
specifies the model selection criterion (statistic of fit) to be used to select from several candidate models. The following list shows the valid values for the CRITERION=option and its corresponding statistic of fit:
- SSE
sum of square error
- MSE
mean square error
- RMSE
root mean squared error
- UMSE
unbiased mean squared error
- URMSE
unbiased root mean squared error
- MAXPE
maximum percent error
- MINPE
minimum percent error
- MPE
mean percent error
- MAPE
mean absolute percent error
- MDAPE
median absolute percent error
- GMAPE
geometric mean absolute percent error
- MINAPES
minimum absolute error percent of standard deviation
- MAXAPES
maximum absolute error percent of standard deviation
- MAPES
mean absolute error percent of standard deviation
- MDAPES
median absolute error percent of standard deviation
- GMAPES
geometric mean absolute error percent of standard deviation
- MINPPE
minimum predictive percent error
- MAXPPE
maximum predictive percent error
- MPPE
mean predictive percent error
- MAPPE
symmetric mean absolute predictive percent error
- MDAPPE
median absolute predictive percent error
- GMAPPE
geometric mean absolute predictive percent error
- MINSPE
minimum symmetric percent error
- MAXSPE
maximum symmetric percent error
- MSPE
mean symmetric percent error
- SMAPE
symmetric mean absolute percent error
- MDASPE
median absolute symmetric percent error
- GMASPE
geometric mean absolute symmetric percent error
- MINRE
minimum relative error
- MAXRE
maximum relative error
- MRE
mean relative error
- MRAE
mean relative absolute error
- MDRAE
median relative absolute error
- GMRAE
geometric mean relative absolute error
- MAXERR
maximum error
- MINERR
minimum error
- ME
mean error
- MAE
mean absolute error
- MASE
mean absolute scaled error
- RSQUARE
R square
- ADJRSQ
adjusted R square
- AADJRSQ
Amemiya’s adjusted R square
- RWRSQ
random walk R square
- AIC
Akaike information criterion
- AICC
finite sample corrected AIC
- SBC
Schwarz Bayesian information criterion
- APC
Amemiya’s prediction criterion
- ENDZEROS=(MAXNUM=number MAXPCT=percent MINOBS=number)
-
specifies criteria that control the use of a _ZERO_ model for a series. A _ZERO_ model is used when the number of trailing zero values in the series fails to indicate that a nonzero model is appropriate. (By default, no _ZERO_ model testing is performed.) If CHOOSE= is specified in the model selection list that is used for a series, these options have no effect and the chosen model is used.
You can specify the following suboptions:
- MAXNUM=number
specifies the count of trailing zero values needed in order to consider a nonzero model. If the series has number or fewer trailing zero observations, then a nonzero model is considered. For example, if you specify ENDZEROS=(MAXNUM=10), then a nonzero model is considered only if the series has 10 or fewer trailing zero values.
- MAXPCT=P
specifies the maximum percentage of trailing zero values relative to the number of nonzero values in the entire series. If the number of trailing zero observations in the series is fewer than or equal to P% of the nonmissing and nonzero values of the entire series, then a nonzero model is considered. The value of P is in the range (0,100). If you specify ENDZEROS=(MAXPCT=20), then a nonzero model is considered only if the number of trailing zero values is fewer than or equal to 20% of the number of nonmissing and nonzero values of the entire series.
- MINOBS=number
specifies the minimum series length that is required to perform the _ZERO_ model test. For example, if you specify MINOBS=8, then the _ZERO_ model test is not performed on any series whose length is fewer than eight observations. By default, MINOBS=0, which implies no minimum length exclusion.
If MAXNUM=0 and MAXPCT=100 or MAXPCT=0, then a nonzero model is always considered. You can specify a combination of the MAXNUM= and MAXPCT= options. If you specify ENDZEROS=(MAXNUM=10 MAXPCT=20), then a nonzero model is considered only if the series has 10 or fewer trailing zero values and if the number of trailing zero values is fewer than or equal to 20% of the number of nonmissing and nonzero values of the entire series.
When a series fails to use a nonzero model by the criteria specified in this option, the _ZERO_ model is used. In the OUTEST= data set, the _MODEL_ variable is set to _ZERO_, and the remaining data set variables that are associated with the parameter estimates are set to missing.
- HOLDOUT=n
specifies the size of the holdout sample to be used for model selection. The holdout sample is a subset of actual time series that ends at the last nonmissing observation.
- HOLDOUTPCT=number
specifies the size of the holdout sample as a percentage of the length of the time series. If HOLDOUT=5 and HOLDOUTPCT=10, the size of the holdout sample is
, where T is the length of the time series with beginning and ending missing values removed. - INTERMITTENT=number
specifies a number greater than 1 that is used to determine whether or not a time series is intermittent. If the median demand interval is equal to or greater than this number, then the series is assumed to be intermittent.
- MINOBS=(TREND=n )
-
specifies that no trend model be fit to any series with fewer than n nonmissing values. Normally the models in a selection list are not subset by trend.
Incorporation of a trend is checked only for smoothing, UCM, and ARIMA models. For the smoothing case, only simple smoothing is a non-trend model. For UCM, the absence of a slope component qualifies it as a non-trend model. For ARIMA, there must be no differencing of the dependent variable for PROC HPFENGINE to consider it a non-trend model.
The value of n must be greater than or equal to 1. The default is TREND=1.
- MINOBS=(SEASON=n )
specifies that no seasonal model be fit to any series with fewer observations than n multiplied by the seasonal cycle length. The value of n must be greater than or equal to 1. The default is SEASON=2.
- MINOBS=n
specifies that any series with fewer than n nonmissing values not be fit using the models in the selection list, but instead be forecast as the mean of the observations in the series. The forecast is recorded as using the _MEAN_ model in the OUTEST= data set _MODEL_ variable. The value of n must be greater than or equal to 1. The default is MINOBS=1.
- NOALTLIST
disables the default action and sets the forecast to missing if no models can be fit from the initial selection list. (By default, if none of the models in a selection list can be successfully fit to a series, PROC HPFENGINE returns to the selection list Sashelp.Hpfdflt.Best and restarts the selection process.) There will be an observation in OUTSUM=, if requested, that corresponds to the variable and BY group in question, and the _STATUS_ variable will be nonzero.
- OVERRIDE
forces the use of any options listed.
- SEASONTEST=option
-
specifies the options related to the seasonality test.
The following values for the SEASONTEST= option are allowed:
- NONE
no test
- (SIGLEVEL=number)
specifies the significance probability value to use in testing whether seasonality is present in the time series. The value must be between 0 and 1. A smaller value of the SIGLEVEL= option means that stronger evidence of a seasonal pattern in the data is required before PROC HPFENGINE uses seasonal models to forecast the time series.
The default is SEASONTEST=(SIGLEVEL=0.01).
The following details apply to the usage of SEASONTEST=(SIGLEVEL=number):
If the HPFDIAGNOSE procedure is used to identify a single model, and that model is input to the HPFENGINE procedure by using the REPOSITORY= option and the INEST= option, then SEASONTEST(SIGLEVEL=) does not apply. Note that the default SIGLEVEL= value for PROC HPFDIAGNOSE is 0.05.
If no model is specified for PROC HPFENGINE, then the procedure chooses a model from the list of default smoothing models. In this case, SEASONTEST(SIGLEVEL=) does apply, and SIGLEVEL=0.01 is the default value for choosing between a seasonal and nonseasonal model.
If a model that is included in the PROC HPFENGINE selection list is created using the METHOD=BEST option of the ESM statement in the HPFESMSPEC procedure, then the significance level for including seasonal components in the model is fixed at 0.05. The SEASONTEST(SIGLEVEL=) value in PROC HPFENGINE does not affect the identification of seasonal components in the BEST model when you use this method.