public double getModelAIC(List <double[]> vec, double[] data, int type)
{
int n = data.Length;
int p = 0;
int q = 0;
double tmpAR = 0.0;
double tmpMA = 0.0;
double sumErr = 0.0;
if (type == 1)
{
double[] maCoe = vec[0];
q = maCoe.Length;
double[] errData = new double[q];
for (int i = q - 1; i < n; i++)
{
tmpMA = 0.0;
for (int j = 1; j < q; j++)
{
tmpMA += maCoe[j] * errData[j];
}
for (int j = q - 1; j > 0; j--)
{
errData[j] = errData[j - 1];
}
errData[0] = ARMAFoundation.gaussrand0() * Math.Sqrt(maCoe[0]);
sumErr += (data[i] - tmpMA) * (data[i] - tmpMA);
}
return((n - (q - 1)) * Math.Log(sumErr / (n - (q - 1))) + (q + 1) * 2);
}
else if (type == 2)
{
double[] arCoe = vec[0];
p = arCoe.Length;
for (int i = p - 1; i < n; ++i)
{
tmpAR = 0.0;
for (int j = 0; j < p - 1; ++j)
{
tmpAR += arCoe[j] * data[i - j - 1];
}
sumErr += (data[i] - tmpAR) * (data[i] - tmpAR);
}
//return Math.log(sumErr) + (p + 1) * 2 / n;
return((n - (p - 1)) * Math.Log(sumErr / (n - (p - 1))) + (p + 1) * 2);
}
else
{
double[] arCoe = vec[0];
double[] maCoe = vec[1];
p = arCoe.Length;
q = maCoe.Length;
var errData = new double[q];
for (int i = p - 1; i < n; ++i)
{
tmpAR = 0.0;
for (int j = 0; j < p - 1; ++j)
{
tmpAR += arCoe[j] * data[i - j - 1];
}
tmpMA = 0.0;
for (int j = 1; j < q; ++j)
{
tmpMA += maCoe[j] * errData[j];
}
for (int j = q - 1; j > 0; --j)
{
errData[j] = errData[j - 1];
}
errData[0] = ARMAFoundation.gaussrand0() * Math.Sqrt(maCoe[0]);
sumErr += (data[i] - tmpAR - tmpMA) * (data[i] - tmpAR - tmpMA);
}
//return Math.log(sumErr) + (q + p + 1) * 2 / n;
return((n - (q + p - 1)) * Math.Log(sumErr / (n - (q + p - 1))) + (p + q) * 2);
}
}
public int predictValue(int p, int q, int period)
{
var data = this.preDealDiff(period);
int n = data.Length;
int predict = 0;
double tmpAR = 0.0;
double tmpMA = 0.0;
double[] errData = new double[q + 1];
if (p == 0)
{
List <double> maCoe = new List <double>(this.arima[0]);
for (int k = q; k < n; ++k)
{
tmpMA = 0;
for (int i = 1; i <= q; ++i)
{
tmpMA += maCoe[i] * errData[i];
}
//
for (int j = q; j > 0; --j)
{
errData[j] = errData[j - 1];
}
errData[0] = ARMAFoundation.gaussrand0() * Math.Sqrt(maCoe[0]);
}
predict = (int)(tmpMA); //
}
else if (q == 0)
{
List <double> arCoe = new List <double>(this.arima[0]);
for (int k = p; k < n; ++k)
{
tmpAR = 0;
for (int i = 0; i < p; ++i)
{
tmpAR += arCoe[i] * data[k - i - 1];
}
}
predict = (int)(tmpAR);
}
else
{
List <double> arCoe = new List <double>(this.arima[0]);
List <double> maCoe = new List <double>(this.arima[1]);
for (int k = p; k < n; ++k)
{
tmpAR = 0;
tmpMA = 0;
for (int i = 0; i < p; ++i)
{
tmpAR += arCoe[i] * data[k - i - 1];
}
for (int i = 1; i <= q; ++i)
{
tmpMA += maCoe[i] * errData[i];
}
//
for (int j = q; j > 0; --j)
{
errData[j] = errData[j - 1];
}
errData[0] = ARMAFoundation.gaussrand0() * Math.Sqrt(maCoe[0]);
}
predict = (int)(tmpAR + tmpMA);
}
return(predict);
}
