public static Result TestSpearmanMethod(List <double> excerpt)
{
double[,] h = new double[excerpt.Count - 1, excerpt.Count - 1];
for (int i = 0; i < excerpt.Count - 1; i++)
{
for (int j = i + 1; j < excerpt.Count; j++)
{
if (excerpt[i] == excerpt[j])
{
h[i, j - 1] = 0.5;
}
else if (excerpt[i] < excerpt[j])
{
h[i, j - 1] = 1;
}
}
}
double v = 0;
for (int i = 0; i < excerpt.Count - 1; i++)
{
for (int j = i + 1; j < excerpt.Count; j++)
{
v += (j - 1) * h[i, j - 1];
}
}
double rc = 1 - 12 * v / excerpt.Count / (excerpt.Count * excerpt.Count - 1);
double d = 1.0 / (excerpt.Count - 1);
double s = rc / Math.Sqrt(d);
double q = DistributionHelper.GetNormalDistributionQuantile(TimeSeriesEnvironment.Current.Alpha);
return(new Result(q, s));
}
public static StatEstimationResult StatEstimation(Queue q, int m)
{
int n = q.Count;
List <double> times = q.ToTime;
double dt = times.Last() / m;
List <StatEstimationClassResult> intervals = new List <StatEstimationClassResult>();
double ua = DistributionHelper.GetNormalDistributionQuantile(Globals.Alpha / 2);
double constantPart = ua * ua / n / dt / 2;
for (int i = 0; i < m; i++)
{
int ns = times.Count(x => x >= dt * i && (i == m - 1 || x < dt * (i + 1)));
double us = ns / dt / n;
double difPart = ua * Math.Sqrt(ns + ua * ua / 4) / n / dt;
ConfidenceInterval cnf = new ConfidenceInterval(us, x => x + constantPart - difPart, x => x + constantPart + difPart, dt * i, dt * (i + 1));
intervals.Add(new StatEstimationClassResult(i + 1, cnf));
}
return(new StatEstimationResult(intervals));
}
public static Result TestSignsMethod(List <double> excerpt)
{
double l = 0,
m = (excerpt.Count - 1) / 2,
d = (excerpt.Count + 1) / 12;
for (int i = 0; i < excerpt.Count - 1; i++)
{
if (excerpt[i] == excerpt[i + 1])
{
l += 0.5;
}
else if (excerpt[i] > excerpt[i + 1])
{
l += 1;
}
}
double u = (l - m) / Math.Sqrt(d);
double q = DistributionHelper.GetNormalDistributionQuantile(TimeSeriesEnvironment.Current.Alpha);
return(new Result(q, u));
}