public List <double[]> Fit()
{
var vec = new List <double[]>();
ARMAFoundation ar_math = new ARMAFoundation();
var armaCoe = ar_math.computeARMACoe(this.data, this.p, this.q);
var arCoe = new double[this.p + 1];
for (int i = 0; i < arCoe.Length; i++)
{
arCoe[i] = armaCoe[i];
}
var maCoe = new double[this.q + 1];
for (int i = 0; i < maCoe.Length; i++)
{
maCoe[i] = armaCoe[i + this.p + 1];
}
//aggregate coefficients
vec.Add(arCoe);
vec.Add(maCoe);
return(vec);
}
public override double[] Fit()
{
//
ARMAFoundation ar_math = new ARMAFoundation();
var maCoe = ar_math.computeARCoe(this.data, this.param);
return(maCoe);
}
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[] getARIMAModel(int period, List <int[]> notModel, bool needNot)
{
var data = this.preDealDiff(period);
//
double minAIC = 1.7976931348623157E308;
var bestModel = new int[3];
int type = 0;
List <double[]> coe = new List <double[]>();
// The model is generated, that is, the corresponding p, q parameters are generated
int len = data.Length > 5 ? 5 : data.Length;
int size = ((len + 2) * (len + 1)) / 2 - 1;
List <int[]> model = new List <int[]>();
//
for (int i = 0; i < size; i++)
{
model.Add(new int[size]);
}
int cnt = 0;
for (int i = 0; i <= len; ++i)
{
for (int j = 0; j <= len - i; ++j)
{
if (i == 0 && j == 0)
{
continue;
}
model[cnt][0] = i;
model[cnt++][1] = j;
}
}
//
for (int i = 0; i < cnt; ++i)
{
// Control selected parameters
bool token = false;
if (needNot)
{
for (int k = 0; k < notModel.Count; ++k)
{
if (model[i][0] == notModel[k][0] && model[i][1] == notModel[k][1])
{
token = true;
break;
}
}
}
if (token)
{
continue;
}
if (model[i][0] == 0)
{
MAModel ma = new MAModel(data, model[i][1]);
//std::vector<std::vector<double>>
var maC = ma.Fit();
coe.Add(maC);
type = 1;
}
else if (model[i][1] == 0)
{
ARModel ar = new ARModel(data, model[i][0]);
//
var maC = ar.Fit();
coe.Add(maC);
type = 2;
}
else
{
//
ARMAModel arma = new ARMAModel(data, model[i][0], model[i][1]);
//
coe = arma.Fit();
type = 3;
}
ARMAFoundation ar_math = new ARMAFoundation();
double aic = ar_math.getModelAIC(coe, data, type);
// If the order is too long during the solution process, NAN or infinity may occur
if (aic <= 1.7976931348623157E308 && !double.IsNaN(aic) && aic < minAIC)
{
minAIC = aic;
// std::cout<<aic<<std::endl;
bestModel[0] = model[i][0];
bestModel[1] = model[i][1];
bestModel[2] = (int)Math.Round(minAIC);
this.arima = coe;
}
}
return(bestModel);
}
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);
}