public static void prHallway() {
String dir = "result0527/PRHallway0527/";
hallway();
String name = "prHallway";
Averager.averager(dir);
Smoother.smoother(dir, halfSize);
GnuPlotter.pr(dir, name, ranges, pi);
}
public static void bayesWargus() {
String dir = "result0506/BayesWargus0506/";
wargus();
String name = "bayesWargus";
Averager.averager(dir);
Smoother.smoother(dir, halfSize);
GnuPlotter.bayes(dir, name, ranges, pi);
}
public static void prTaxi() {
String dir = "result0527/PRTaxi0527/";
taxi();
String name = "prTaxi";
Averager.averager(dir);
Smoother.smoother(dir, halfSize);
GnuPlotter.pr(dir, name, ranges, pi);
}
public static void main(String[] args) {
// bayesTaxi();
// prTaxi();
// prHallway();
// bayesWargus();
// BayesTaxiComp/BayesTaxi0529
String dir = "BayesTaxi0530_epsilon/";
taxi();
String name = "bayesTaxi";
// String dir = "wargus0529/Wargus3322-n0.15-BMaxQPrNo-1000(2012-05-29)/";
// wargus();
// String name = "bayesWargus";
Averager.averager(dir);
Smoother.smoother(dir, halfSize);
GnuPlotter.bayes(dir, name, ranges, pi);
}
// time-varying trading days
// @Test
public void demoTD() {
TsData s = Data.X;
CompositeResults rslts = TramoSeatsProcessingFactory.process(s, TramoSeatsSpecification.RSA5);
PreprocessingModel regarima = rslts.get("preprocessing", PreprocessingModel.class);
SeatsResults seats = rslts.get("decomposition", SeatsResults.class);
assertTrue(seats != null && regarima != null);
if (regarima.isMultiplicative()) {
s = s.log();
}
int[] calPos =
regarima.description.getRegressionVariablePositions(ComponentType.CalendarEffect);
UcarimaModel ucm = seats.getUcarimaModel();
// compute the full decomposition...
SsfUcarima stoch = new SsfUcarima(ucm);
ExtendedSsfData xdata = new ExtendedSsfData(new SsfData(s, null));
xdata.setForecastsCount(s.getFrequency().intValue());
Matrix x =
regarima
.description
.buildRegressionVariables()
.all()
.matrix(new TsDomain(s.getStart(), xdata.getCount()));
RegSsf xssf = new RegSsf(stoch, x.subMatrix());
Filter filter = new Filter();
filter.setInitializer(new DiffuseSquareRootInitializer());
filter.setSsf(xssf);
DiffuseFilteringResults fr = new DiffuseFilteringResults(true);
fr.getVarianceFilter().setSavingP(true);
fr.getFilteredData().setSavingA(true);
filter.process(xdata, fr);
Smoother smoother = new Smoother();
smoother.setSsf(xssf);
smoother.setCalcVar(true);
SmoothingResults sm = new SmoothingResults();
smoother.process(xdata, fr, sm);
Smoother lsmoother = new Smoother();
lsmoother.setSsf(stoch);
lsmoother.setCalcVar(true);
SmoothingResults lsm = new SmoothingResults();
ExtendedSsfData xldata =
new ExtendedSsfData(new SsfData(regarima.linearizedSeries(false), null));
xldata.setForecastsCount(s.getFrequency().intValue());
lsmoother.process(xldata, lsm);
int spos = stoch.cmpPos(1);
DataBlock Z = new DataBlock(xssf.getStateDim());
double[] v = new double[xdata.getCount()];
double[] c = new double[xdata.getCount()];
double[] svar = sm.componentVar(spos);
double[] slvar = lsm.componentVar(spos);
int start = regarima.description.getRegressionVariablesStartingPosition();
for (int i = 0; i < v.length; ++i) {
Z.set(spos, 1);
for (int j = 0; j < calPos.length; ++j) {
Z.set(stoch.getStateDim() + calPos[j], x.get(i, calPos[j]));
}
v[i] = sm.zvariance(i, Z);
Z.set(spos, 0);
c[i] = sm.zvariance(i, Z);
System.out.print(svar[i]);
System.out.print('\t');
System.out.print(slvar[i]);
System.out.print('\t');
System.out.print(c[i]);
System.out.print('\t');
System.out.println(v[i]);
}
System.out.println(sm.P(50));
System.out.println(sm.P(svar.length - 1));
System.out.println(regarima.estimation.getLikelihood().getBVar());
}
