//r = Cov(x,y)/(StandardDeviation(x)*StandardDeviation(y))
private double computePearsonCorr(Vector x, Vector y)
{
int n = x.Tuples;
Vector xy = new Vector(n);
for (int i = 0; i < n; i++)
xy[i] = x[i] * y[i];
double sumxy = xy.GetSumData();
double xbar = x.GetSumData() / (double)n;
double ybar = y.GetSumData() / (double)n;
double covxy = (sumxy - n * xbar * ybar) / (double)(n - 1);
Vector x2 = x.GetDataSquare();
Vector y2 = y.GetDataSquare();
double varx = (n * x2.GetSumData() - Math.Pow(x.GetSumData(), 2.0)) / (double)(n * (n - 1));
double vary = (n * y2.GetSumData() - Math.Pow(y.GetSumData(), 2.0)) / (double)(n * (n - 1));
return covxy / Math.Sqrt(varx * vary);
}
/// <summary>
/// Constructor. Menghitung nilai LjungBox Q Stat.
/// </summary>
/// <param name="Y">Vektor. Vektor data yang akan dicari
/// nilai LjungBox Q Statistiknya. </param>
/// <param name="Lag">Int. Jumlah lag yang di include kan</param>
public LjungBoxQTest(Vector Y, int Lag)
{
double[] ljung = new double[Lag];
double[] acfValue = new double[Lag];
double sekuadrat;
double sumacf = 0;
double[] acfD;
this.acf = new Vector(Lag);
this.pacf = new Matrix (Lag,Lag);
this.se = new Vector(Lag);
this.lBox = new Vector(Lag);
this.pValue = new Vector(Lag);
this.pValue1 = new Vector(Lag);
this.n = Y.Tuples;
this.lag = Lag;
this.y = Y.Copy();
this.sumY = y.GetSumData();
this.yBar = sumY / n;
// Penghitungan nilai ACF
for (int k = 0; k < lag; k++)
{
acf[k] = getRho(k+1);
}
double sumRho = 0;
for (int i = 1; i <= lag; i++)
{
sumRho += Math.Pow(acf[i - 1], 2) / (n - i);
lBox[i - 1] = n * (n + 2) * sumRho;
pValue1[i - 1] = ChiSquare.PValue(lBox[i - 1], i);
if (pValue1[i - 1] < 0) pValue[i - 1] = 0;
else pValue[i - 1] = pValue1[i - 1];
}
//previous way
//for (int i = 1; i <= lag; i++)
//{
// double sumRho = 0;
// for (int k = 1; k <= i; k++)
// {
// sumRho += Math.Pow(acf[k - 1], 2) / (n - k);
// }
// lBox[i - 1] = n * (n + 2) * sumRho;
// ChiSquare chi = new ChiSquare(i);
// pValue1[i - 1] = chi.PValue(lBox[i - 1]);
// if (pValue1[i - 1] < 0) pValue[i - 1] = 0;
// else pValue[i - 1] = pValue1[i - 1];
//}
//Penghitungan nilai PACF
pacf[0, 0] = acf[0];
for (int i = 1; i < lag; i++)
{
double sumatas = 0;
double sumbawah = 0;
for (int k = 0; k < i; k++)
{
sumatas += pacf[i - 1, k] * acf[(i - k) - 1];
sumbawah += pacf[i - 1, k] * acf[k];
}
pacf[i, i] = (acf[i] - sumatas) / (1 - sumbawah);
for (int j = 0; j < i; j++)
{
pacf[i, j] = pacf[i - 1, j] - (pacf[i, i] * pacf[i - 1, (i - j) - 1]);
}
}
// previous way
//for (int i = 1; i < lag; i++)
//{
// for (int j = 0; j < i; j++)
// {
// double sumatas = 0;
// double sumbawah = 0;
// for (int k = 0; k < i; k++)
// {
// sumatas += pacf[i - 1, k] * acf[(i - k) - 1];
// sumbawah += pacf[i - 1, k] * acf[k];
// }
// pacf[i, i] = (acf[i] - sumatas) / (1 - sumbawah);
// pacf[i, j] = pacf[i - 1, j] - (pacf[i, i] * pacf[i - 1, (i - j) - 1]);
// }
//}
acfD = acf.GetData();
sumacf = 0;
for (int i = 0; i < lag; i++)
{
sekuadrat = (1 + 2 * sumacf) / y.Tuples;
se[i] = Math.Sqrt(sekuadrat);
sumacf += Math.Pow(acfD[i], 2);
}
// previous way
//for (int i = 0; i < lag; i++)
//{
// sumacf = 0;
// for (int j = 0; j < i; j++)
// {
// sumacf += Math.Pow(acfD[j], 2);
// }
// sekuadrat = (1 + 2 * sumacf) / y.Tuples;
// se[i] = Math.Sqrt(sekuadrat);
//}
}