public static double AverageRawReturn(this DenseVector d)
{
double[] result = RawRateOfReturn(d.ToArray());
var e = new DenseVector(result);
return(e.Mean());
}
public static double Kurtosis(this double[] data)
{
var d = new DenseVector(data);
var num = new DenseVector(d.Count);
var denom = new DenseVector(d.Count);
d.CopyTo(num);
d.CopyTo(denom);
for (int i = 0; i < num.Count; i++)
{
num[i] = Math.Pow(d[i] - d.Mean(), 4);
denom[i] = Math.Pow(d[i] - d.Mean(), 2);
}
return((num.Sum() * num.Count) / (Math.Pow(denom.Sum(), 2)));
}
public static double[] NormalizeZScore(this double[] data)
{
var d = new DenseVector(data);
var result = new DenseVector(d.Count);
d.CopyTo(result);
result = (DenseVector)((result - d.Mean()) / (d.StandardDeviation()));
return(result.ToArray());
}
public static double Covariance(DenseVector x, DenseVector y)
{
var cov = new DenseVector(x.Count);
double xMean = x.Mean();
double yMean = y.Mean();
if (xMean.Equals(double.NaN) || yMean.Equals(double.NaN))
{
return(double.NaN);
}
for (int i = 0; i < x.Count; i++)
{
cov[i] = (x[i] - xMean) * (y[i] - yMean);
}
return(((double)1 / (x.Count - 1)) * cov.Sum());
}
public void VarianceRatioTest(double[] data, int lag = 1, TimeSeriesType cor = TimeSeriesType.HOM)
{
DenseVector x = new DenseVector(data);
//Mean
double mu = x.Mean();
//Variance for 1st Order Difference
double s1 = (x.SubVector(lag, x.Count - lag) - x.SubVector(0, x.Count - 2)).Variance();
double dLag = lag;
double varvrt = double.NaN;
switch (cor)
{
case TimeSeriesType.HOM:
{
varvrt = 2 * (2 * dLag - 1) * (dLag - 1) / (3 * dLag * x.Count);
break;
}
case TimeSeriesType.HET:
{
varvrt = 0;
double sum2 = 0;
for (int j = 0; j < lag; j++)
{
double sum1a = 0; //(x(j+2:n)-x(j+1:n-1)-mu).^2
double sum1b = 0; //(x(2:n-j)-x(1:n-j-1)-mu).^2;
double sum1 = sum1a * sum1b;
double delta = sum1 / (Math.Pow(sum2, 2));
varvrt += 0; //(2*(q(i)-j)/q(i))^2)*delta;
}
break;
}
}
ZScore = (VRatio - 1) / Math.Sqrt(varvrt);
PValue = NormCDF(ZScore);
}
/// <summary>
/// 计算ROTI
/// </summary>
/// <param name="arc"></param>
/// <remarks>
/// 2019.研究台风引起电离层扰动的形态特征.许九靖. 安徽理工大学.
/// </remarks>
public static void CalROTI(ref OArc arc)
{
if (arc is null)
{
return;
}
// 利用相位观测值计算相对STEC信号
for (int i = 1; i < arc.Length; i++)
{
if (Math.Abs(arc[i]["L4"]) < 1e-13 ||
Math.Abs(arc[i + 1]["L4"]) < 1e-13)
{
continue;
}
arc[i]["ltec"] = 9.52437 * (arc[i]["L4"] - arc[i - 1]["L4"]);
}
int order = 9;
int left, right;
left = right = (order - 1) / 2;
Vector <double> seg = new DenseVector(order);
for (int i = left; i < arc.Length - right; i++)
{
for (int j = 0; j < order; j++)
{
seg[j] = arc[i - left + j]["ltec"];
}
arc[i]["roti"] = Math.Sqrt(seg.DotProduct(seg) / order - Math.Pow(seg.Mean(), 2));
}
arc.StartIndex += left + 1;
arc.EndIndex -= right + 1;
}
