// TODO Update with Java 8
 private static boolean isPositive(DataBlock q) {
   for (int i = 0; i < q.getLength(); ++i) {
     if (q.get(i) < State.ZERO) {
       return false;
     }
   }
   return true;
 }
 /**
  * @param t
  * @param state
  */
 @Override
 public void save(final int t, final MState state) {
   m_var.save(t, state);
   m_fdata.save(t, state);
   DataBlock diag = state.F.diagonal();
   for (int i = 0; i < state.E.getLength(); ++i) {
     double r = diag.get(i);
     if (r != 0) {
       addStd(state.E.get(i), r);
     }
   }
 }
Exemplo n.º 3
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 private int check(DataBlock z) {
   int idx = -1;
   for (int i = 0; i < z.getLength(); ++i) {
     if (z.get(i) != 0) {
       if (idx != -1) {
         return -1;
       } else {
         idx = i;
       }
     }
   }
   return idx;
 }
 @Override
 public void save(final int t, final AugmentedPredictionErrors pe) {
   DataBlock U = pe.getTransformedPredictionErrors();
   Matrix L = pe.getCholeskyFactor();
   DataBlock D = L.diagonal();
   Matrix E = pe.E();
   int nvars = E.getColumnsCount();
   n += nvars;
   LogSign sld = D.sumLog();
   det += sld.value;
   Q.subMatrix(0, nd, nd + 1, nd + 1 + nvars).copy(E.subMatrix());
   Q.row(nd).range(nd + 1, nd + 1 + nvars).copy(U);
   ec.tstoolkit.maths.matrices.ElementaryTransformations.fastGivensTriangularize(Q.subMatrix());
 }
Exemplo n.º 5
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  double[] scomponent() {

    DisturbanceSmoother smoother = new DisturbanceSmoother();
    smoother.setSsf(ssf);
    smoother.process(ssfdata);
    SmoothingResults sstates = smoother.calcSmoothedStates();
    double[] cmp = new double[ssfdata.getCount()];
    DataBlock z = new DataBlock(ssf.getStateDim());
    DataBlock zc = z.drop(2, 0);
    for (int i = 0; i < cmp.length; ++i) {
      zc.copy(S.row(i));
      cmp[i] = sstates.zcomponent(i, z);
    }
    return cmp;
  }
Exemplo n.º 6
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 /**
  * @param z
  * @return
  */
 public double[] zvariance(DataBlock z) {
   if (m_P == null) {
     return null;
   }
   if (m_r != z.getLength()) {
     return null;
   }
   int iz = check(z);
   double[] var = new double[m_n - m_start];
   if (iz >= 0) {
     return componentVar(iz);
   } else {
     for (int i = 0; i < m_n - m_start; ++i) {
       var[i] = SymmetricMatrix.quadraticForm(m_P.matrix(i), z) * m_ser2;
     }
   }
   return var;
 }
Exemplo n.º 7
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  /**
   * @param z
   * @return
   */
  public double[] zcomponent(DataBlock z) {
    int iz = check(z);
    if (iz >= 0) {
      return component(iz);
    }
    if (m_a == null) {
      return null;
    }
    if (m_r != z.getLength()) {
      return null;
    }

    int n = m_n - m_start;
    double[] c = new double[n];
    for (int i = 0; i < n; ++i) {
      c[i] = m_a.block(i).dot(z);
    }
    return c;
  }
 /**
  * @param s
  * @param domain
  * @return
  */
 @Override
 public TsData process(TsData s, TsDomain domain) {
   TsDomain rdomain = domain == null ? s.getDomain() : domain;
   TsData out = new TsData(rdomain);
   PeriodIterator pin = new PeriodIterator(s, domain);
   PeriodIterator pout = new PeriodIterator(out);
   int p = 0;
   while (pin.hasMoreElements()) {
     DataBlock bin = pin.nextElement().data;
     DataBlock bout = pout.nextElement().data;
     int nf = 0, len = bin.getLength();
     if (filters[p] != null) {
       nf = filters[p].getUpperBound();
     }
     if (filters[p] != null && 2 * nf < len && (nf < 9 || len >= 20)) {
       filters[p].filter(bin, bout.drop(nf, nf));
       endPointsProcessors[p].process(bin, bout);
     } else {
       bout.set(bin.sum() / len);
     }
     ++p;
   }
   return out;
 }
Exemplo n.º 9
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 @Override
 public void XS(int pos, DataBlock x, DataBlock xs) {
   xs.set(0, std() * x.get(0));
 }
Exemplo n.º 10
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 @Override
 public void addSU(int pos, DataBlock x, DataBlock u) {
   x.add(0, std() * u.get(0));
 }
Exemplo n.º 11
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  // 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());
  }