The same code applied to a different data set with different series doesn't give any problem%let sel_in0 =lscas.level_rp;
%let sel_by =region prefecture;
%let sel_suf =rp_dl;
%let sel_target=avg_download_kbps;
%let fore_x =num_of_cells2G3G added_cells2G3G num_of_cells4G added_cells4G num_of_cells_tot added_cells_tot
weekendgg holidaygg pontegg festivi gg lavorativi;
%let end_fit ="01jul2018"d;
%let sel_back =6;
%let sel_lead =18;
%let sel_in=&sel_in0.;
data _null_;
array conta (*) &fore_x.;
call symput('nvar', compress(dim(conta)));
vars="&fore_x.";
do i=1 to dim(conta);
var=scan(vars,i," ");
call symput('var' || compress(i),compress(var));
end;
run;
%macro atsm;
proc tsmodel
data=&sel_in.
outobj= (
outfor = LSCAS.&sel_suf._LS_OUTFOR /* actual and forecast value*/
outstat = LSCAS.&sel_suf._LS_OUTSTAT /* forecast stat and perfomance */
outmodelinfo = LSCAS.&sel_suf._LS_MODELINFO /* selected model summary */
parmest = LSCAS.&sel_suf._LS_PARMEST /* selected model details */
)
seasonality=12;
*define time series ID variable and the time interval;
id date interval = MONTH FORCEINPUTFORMAT SETMISSING=missing;
*define time series and the corresponding accumulation methods;
var &sel_target. / accumulate=average;
%do v=1 %to &nvar.;
var &&var&v.. / accumulate=average;
%end;
*define the by variables if exist;
by &sel_by.;
*using the ATSM (Automatic Time Series Model) package;
require atsm;
*starting user script;
submit;
*DECLARING THE ATSM OBJECTS;
declare object dataframe(tsdf) ; /* TSDF: Time series data frame used to group series variables for DIAGNOSE and FORENG objects */
declare object diagspec(diagspec); /* DIAGSPEC: Diagnostic control options for DIAGNOSE object */
declare object diagnose(diagnose); /* DIAGNOSE: Automatic time series model generation */
declare object forecast(foreng) ; /* FORENG: Automatic time series model selection and forecasting */
declare object outfor(outfor); /* OUTFOR : Collector for FORENG forecasts */
declare object outstat(outstat); /* OUTSTAT : Collector for FORENG forecast performance statistics */
declare object outmodelinfo(outmodelinfo); /* OUTMODEL: Collector for selected model summary */
declare object parmest(outest); /* PARMEST : Collector for selected model details */
array perror[2] / nosymbols; /* MINIC (ARMA selection) parameters array */
array p[2] / nosymbols; /* AR components parameters array */
array q[2] / nosymbols; /* MA components parameters array */
array ps[2] / nosymbols; /* Seasonal AR components parameters array */
array qs[2] / nosymbols; /* Seasonal MA components parameters array */
array xnum[2] / nosymbols; /* Transfer Function AR components parameters array */
array xden[2] / nosymbols; /* Transfer Function MA components parameters array*/
array diff[2] / nosymbols; /* integration test*/
array shift[2] / nosymbols; /* level shift min duration*/
*INITIALIZE THE TSDF OBJECT AND ASSIGN THE TIME SERIES ROLES;
rc = dataframe.initialize();
/* Target series */
rc = dataframe.addY(&sel_target.);
/* Explantory series */
%do v=1 %to &nvar.;
rc = dataframe.addX(&&var&v.., 'extend','LAST','required','MAYBE','keep',0,'nodiff',0,'control',1,'repmiss',1);
%end;
*OPEN THE DIAGSPEC OBJECT AND ENABLE ESM AND ARIMAX MODEL CLASS FOR DIAGNOSE;
rc = diagspec.open();
rc = diagspec.setOption(
'SIGLEVEL', 0.05 /* Significance level */
,'CRITERION','GMAPE' /* model selection criterion to select the best mode */
,'INPUTMISSPCT',1 /* accetable size (%)of the missing observation of the input time se-ries*/
,'ENTRYPCT', 0.1 /* min percentage increment of the criterion between two candidate models to stop */
,'PREFILTER','EXTREME' /* smoothed values for missing and extreme values prior to diagnostic tests*/
,'TESTINPUT','BOTH' /* log transform and trend testing of the input variables */
,'SELECTINPUT','SELECT' /* maximum number of the input variables to select */
);
PERROR[1]=0;PERROR[2]=24; /* AR order range for MINIC (ARMA selection) error */
P[1] =0;P[2] =12; /* Nonseasonal AR order range */
Q[1] =0;Q[2] =12; /* Nonseasonal MA order range */
PS[1] =0;PS[2] =2; /* Seasonal AR order range */
QS[1] =0;QS[2] =2; /* Seasonal MA order range */
XNUM[1] =0;XNUM[2] =12; /* Transfer Funtcion Numerator order range */
XDEN[1] =0;XDEN[2] =12; /* Transfer Funtcion Denominator order range */
DIFF[1] =0;DIFF[2] =12; /* integration test */
SHIFT[1] =2;SHIFT[2]=6 ; /* integration test */
rc=diagspec.setTrend('SIGLEVEL', 0.05, 'DIFFRAN',DIFF, 'SDIFF','AUTO');
rc = diagspec.setARIMAX(
'SIGLEVEL', 0.2 /* ARIMAX parameters significance*/
,'IDENTIFY','BOTH' /* Identification order (ARIMA, REG, or BOTH) */
,'NOINT',0 /* Suppress constant term (0 | 1) */
,'PERROR',PERROR /* AR order range for MINIC (tentative ARMA order selection) error */
,'P',P /* Nonseasonal AR order range */
,'Q',Q /* Nonseasonal MA order range */
,'PS',PS /* Seasonal AR order range */
,'QS',QS /* Seasonal MA order range */
,'XNUM',XNUM /* Transfer Funtcion Numerator order range */
,'XDEN',XDEN /* Transfer Funtcion Denominator order range */
,'CRITERION','SBC' /* Identification criterion */
,'ESTMETHOD','CLS' /* ARIMA estimation method */
,'METHOD','MINIC' /* Tentative ARMA orders */
);
/* outliers detections */
rc=diagspec.setARIMAXOutlier(
'ALLOWAO',1
,'ALLOWLS',1
,'ALLOWTLS',1
,'DETECT','MAYBE'
,'ENTRYPCT',0.1
,'FILTER','FULL'
,'MAXPCT',5
,'SIGLEVEL',0.01
,'TLSVALS',SHIFT);
/* refine parameters of selected model excluding outliers */
rc = diagspec.setARIMAXRefine(
'SIGLEVEL',0.4
,'ORDER','ARMA:INPUT');
/* UCM */
rc = diagspec.setUCM( 'SIGLEVEL', 0.05
,'IRREGULAR',1
,'LEVEL',1
,'SLOPE',1
,'SEASON',1
,'CYCLE',1
,'AUTOREG',1
,'DEPLAG',1
);
/* refine parameters of selected model excluding outliers */
rc=diagspec.setUCMRefine('SIGLEVEL',0.4,'ORDER','INPUT');
/* cominbed model specification */
rc=diagspec.setCombine();
rc = diagspec.Close();
*SET THE DIAGNOSE OBJECT USING THE DIAGSPEC OBJECT AND RUN THE DIAGNOSE PROCESS;
rc = diagnose.initialize(dataframe);
rc = diagnose.setSpec(diagspec); /* use diagspec defined above to run diagnose */
rc = diagnose.SetOption('HOLDOUT', &sel_back.); /* number of periods to be used only for model selection, not identification */
rc = diagnose.SetOption('BACK' , &sel_back.); /* number of periods to be used for forecast (as if actuals does not exist) */
rc = diagnose.run();
*INITIALIZE THE FORENG OBJECT WITH THE DIAGNOSE RESULT AND RUN MODEL SELECTING AND GENERATE FORECASTS;
rc = forecast.initialize(diagnose); /* use diagnose result for forecasting */
rc = forecast.setOption('ALPHA', 0.05); /* fit statistics*/
rc = forecast.setOption('CRITERION','GMAPE'); /* fit statistics*/
rc = forecast.SetOption('HOLDOUT', &sel_back.); /* number of periods to be used only for model selection, not identification */
rc = forecast.SetOption('BACK', &sel_back.); /* number of periods to be used for forecast (as if actuals does not exist) */
rc = forecast.setOption('LEAD', &sel_lead.); /* number of periods (from back) for which has to be calculate forecast */
rc = forecast.setOption('MINOBS.TREND',12); /*no trend model is fitted to any series with fewer nonmissing obs */
rc = forecast.setOption('MINOBS.SEASON',24); /*no season model is fitted to any series with fewer nonmissing obs */
rc= forecast.setOption('TASK','FORECAST'); /* SELECT=model selection, estimates parameters,produces forecasts
FIT=estimates parameters by using the model specified then forecasts
UPDATE=estimates parameters by using the model specified then forecasts.
UPDATE differs from FIT, parameters found in specified are used
as starting values in estimation
FORECAST=forecasts using model and parameters specified*/
rc = forecast.run();
*COLLECT THE FORECAST AND STATISTIC-OF-FIT FROM THE FORGEN OBJECT RUN RESULTS;
rc = outfor.collect(forecast); /* OUTFOR : load forecasts */
rc = outstat.collect(forecast); /* OUTSTAT : load forecast performance statistics */
rc = outmodelinfo.collect(forecast); /* OUTMODEL: load selected model summary */
rc = parmest.collect(forecast); /* PARMEST : load selected model details */
endsubmit;
quit;
%mend atsm;
option mprint;
%atsm;
