// please before call this fuction set 2 properties and then call generate method
public MyTimeSeriForBestHybrid <T> generateForBestHybrid(int numberOfForecastTests)
{
MyTimeSeriForBestHybrid <T> tmp = new MyTimeSeriForBestHybrid <T>();
int leng = TimeSeri.Length - numberOfForecastTests;
int l = 0;
for (; l < leng / 2; l++)
{
tmp.part1.Add(TimeSeri[l]);
}
for (; l < leng; l++)
{
tmp.part2.Add(TimeSeri[l]);
}
for (; l < TimeSeri.Length; l++)
{
tmp.testCases.Add(TimeSeri[l]);
}
return(tmp);
}
void Start()
{
var Et = new List <double>();
var Zt = new List <double>();
var Lt = new List <double>();
timeSeriGenerator = new TimeSeriGenerator <double>();
arimaLogger = new StreamWriter("Best_Hybrid_ArimaGALog.txt");
int numInp = 0;
this.Dispatcher.Invoke(new Action(() => numInp = Int32.Parse(NumberOfInpTextBox.Text)));
timeSeriGenerator.load(numInp);
Dispatcher.Invoke(new Action(() => ActivityProgressBar.IsIndeterminate = true));
Dispatcher.Invoke(new Action(() => numberOfTests = Int32.Parse(OptimumTestTextBox.Text)));
Dispatcher.Invoke(new Action(() => numberOfForecastTests = Int32.Parse(ForecastTestTextBox.Text)));
MyTimeSeriForBestHybrid <double> myTimeSeriForBestHybrid =
timeSeriGenerator.generateForBestHybrid(numberOfForecastTests);
//maxGAIteretionInArima = 1000;
//var train = new double[timeSeriGenerator.TimeSeri.Length - 5];
//var test = new double[5];
//for (int i = 0; i < train.Length; i++)
//{
// train[i] = timeSeriGenerator.TimeSeri[i];
//}
//for (int i = train.Length, j = 0; i < timeSeriGenerator.TimeSeri.Length; i++, j++)
//{
// test[j] = timeSeriGenerator.TimeSeri[i];
//}
//ArimaGA aga=new ArimaGA();
//aga.StartArima(train);
//NumericalVariable timeSeriii = new NumericalVariable("timeSeriii", train);
//arimaModel = new ArimaModel(timeSeriii, aga.bestP, aga.bestD, aga.bestQ);
//arimaModel.Compute();
//var fv2 = arimaModel.Forecast(numberOfForecastTests);
//double ea2 = MyErrorParameters.ERROR_Percent(fv2.ToArray(), test);)
for (int i = 0; i < myTimeSeriForBestHybrid.part2.Count; i++)
{
StartArima(myTimeSeriForBestHybrid.part1.ToArray());
//// converting to double[]
//double[] db = new double[myTimeSeriForBestHybrid.part1.Count];
//for (int j = 0; j < db.Length; j++)
//{
// db[j] = myTimeSeriForBestHybrid.part1.ToArray()[j];
//}
NumericalVariable timeSerii = new NumericalVariable("timeSerii", myTimeSeriForBestHybrid.part1.ToArray());
arimaModel = new ArimaModel(timeSerii, myBestP, myBestD, myBestQ);
arimaModel.Compute();
var res = arimaModel.Forecast(1);
float lt = (float)res[0];
Lt.Add(lt);
double target = myTimeSeriForBestHybrid.part2[i];
double e = lt - target;
Et.Add(e);
myTimeSeriForBestHybrid.part1.Add(target);
double mu = myTimeSeriForBestHybrid.part1.Average();
if (myBestD == 0)
{
Zt.Add(myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 1] - mu);
}
else if (myBestD == 1)
{
Zt.Add(myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 1] -
myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 2] - mu);
}
else
{
Zt.Add(myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 1] -
2 * myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 2] +
myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 3] - mu);
}
}
ArimaModel EtArimaModel = new ArimaGA().GetBestModel(Et.ToArray());
ArimaModel ZtArimaModel = new ArimaGA().GetBestModel(Zt.ToArray());
int a = 0;
SVM svm = new SVM();
//TimeSeriGenerator<double> gen = new TimeSeriGenerator<double>();
//gen.NumberOfInputVariables = Int32.Parse(NumberOfInpTextBox.Text);
//gen.TimeSeri = Et.ToArray();
//var EtTimeSeries = gen.generate();
//gen = new TimeSeriGenerator<double>();
//gen.NumberOfInputVariables = Int32.Parse(NumberOfInpTextBox.Text);
//gen.TimeSeri = Zt.ToArray();
//var ZtTimeSeries = gen.generate();
//// biaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
Pair <int> bestAB = CreateComplexHybridModel(Et.ToArray(), Lt.ToArray(), Zt.ToArray());
double minErr = SVMComplexModelForBestModel(bestAB.First, bestAB.Second, Et.ToArray(), Lt.ToArray(),
Zt.ToArray());
MessageBox.Show(bestAB.First + " , " + bestAB.Second + "\nMinError In Training Is => " + minErr, "Now Best M & N Found", MessageBoxButton.OK,
MessageBoxImage.Asterisk);
// --------------------------------- now our complex hybrid model is created -----------------------------------------------------------------
double mse = 0;
double errorPercent = 0;
double sumTargets = 0;
if (myTimeSeriForBestHybrid.part1.Count != timeSeriGenerator.TimeSeri.Length - numberOfForecastTests)
{
MessageBox.Show("Input For Arima Model Is Not Completed", "ERROR", MessageBoxButton.OK, MessageBoxImage.Error);
}
// << CHECK HERE >> (FOR CHECKING PURPOSE ONLY , COMMENT HERE LATER)
var forecastedVector = arimaModel.Forecast(numberOfForecastTests);
double eoa = MyErrorParameters.ERROR_Percent(forecastedVector.ToArray(), myTimeSeriForBestHybrid.testCases.ToArray());
MessageBox.Show("Error Of Arima Is => " + eoa, "Arima Error", MessageBoxButton.OK,
MessageBoxImage.Information);
//maxGAIteretionInArima = 1000;
//StartArima(myTimeSeriForBestHybrid.part1.ToArray());
//double[] dbb = new double[myTimeSeriForBestHybrid.part1.Count];
//for (int j = 0; j < dbb.Length; j++)
//{
// dbb[j] = myTimeSeriForBestHybrid.part1.ToArray()[j];
//}
//NumericalVariable timeSeriTest = new NumericalVariable("timeSerii", dbb);
//arimaModel = new ArimaModel(timeSeriTest, myBestP, myBestD, myBestQ);
//arimaModel.Compute();
StreamWriter hybridWriter = new StreamWriter(OUTPUT_FILE_NAME);
List <double> results = new List <double>();
//double errorOfArima = MyErrorParameters.ERROR_Percent(forcastedVector.ToArray(), myTimeSeriForBestHybrid.testCases.ToArray());
//MessageBox.Show("Error Of Arima Is => " + errorOfArima, "Arima Error", MessageBoxButton.OK,
// MessageBoxImage.Information);
// ---------------------------------------------------------------
int numOfInp = bestAB.First + bestAB.Second + 1;
int rows = Et.Count - Math.Max(bestAB.First, bestAB.Second);
float[,] inps = new float[rows, numOfInp];
double[] targs = new double[rows];
int y = bestAB.First;
int z = bestAB.Second;
int ll = 0;
for (int o = 0; o < rows; o++)
{
if (y > z)
{
for (int j = 0; j < y; j++)
{
inps[o, j] = (float)Et[ll + j];
}
inps[o, y] = (float)Lt[ll + y];
for (int j = 0; j < z; j++)
{
inps[o, y + j + 1] = (float)Zt[ll + y - z + j];
}
targs[o] = timeSeriGenerator.TimeSeri[ll + y];
}
else
{
for (int j = 0; j < y; j++)
{
inps[o, j] = (float)Et[ll + z - y + j];
}
inps[o, y] = (float)Lt[ll + z];
for (int j = 0; j < z; j++)
{
inps[o, j + y + 1] = (float)Zt[ll + j];
}
targs[o] = timeSeriGenerator.TimeSeri[ll + z];
}
ll++;
}
float[,] trainInputs = new float[rows - numberOfTests, numOfInp];
float[] trainTargets = new float[rows - numberOfTests];
float[,] testInputs = new float[numberOfTests, numOfInp];
float[] testTargets = new float[numberOfTests];
int t = 0;
for (; t < rows - numberOfTests; t++)
{
for (int j = 0; j < numOfInp; j++)
{
trainInputs[t, j] = inps[t, j];
}
trainTargets[t] = (float)targs[t];
}
for (int o = 0; t < rows; o++, t++)
{
for (int j = 0; j < numOfInp; j++)
{
testInputs[o, j] = inps[t, j];
}
testTargets[o] = (float)targs[t];
}
svmModelHybrid = new SVM();
SVM_KERNEL_TYPE bestKernelType = SVM_KERNEL_TYPE.RBF;
double bestEps = 0.001;
SVMParams p;
Matrix <float> trainData = new Matrix <float>(trainInputs);
Matrix <float> trainClasses = new Matrix <float>(trainTargets);
p = new SVMParams();
p.KernelType = bestKernelType;
p.SVMType = Emgu.CV.ML.MlEnum.SVM_TYPE.EPS_SVR; // for regression
p.C = 1;
p.TermCrit = new MCvTermCriteria(100, bestEps);
p.Gamma = 1;
p.Degree = 1;
p.P = 1;
p.Nu = 0.1;
bool _trained = svmModelHybrid.TrainAuto(trainData, trainClasses, null, null, p.MCvSVMParams, 10);
// ---------------------------------------------------------------
for (int i = 0; i < numberOfForecastTests; i++)
{
float[,] inpTest = new float[bestAB.First + bestAB.Second + 1, 1];
int l = 0;
for (int j = 0; j < bestAB.First; j++, l++)
{
inpTest[l, 0] = (float)Et[Et.Count - bestAB.First + j];
}
inpTest[l++, 0] = (float)forecastedVector[i];
for (int j = 0; j < bestAB.Second; j++, l++)
{
inpTest[l, 0] = (float)Zt[Zt.Count - bestAB.Second + j];
}
// injaaaaaaaaaaaaaaaaaaaa
float result = svmModelHybrid.Predict(new Matrix <float>(inpTest));
results.Add(result);
hybridWriter.WriteLine(result);
double target = myTimeSeriForBestHybrid.testCases[i];
// preparing for next use in this for loop
double resi = target - (float)forecastedVector[i]; // float resi = target - result; << CHECK HERE IMPORTANT >>
Et.Add(resi);
myTimeSeriForBestHybrid.part1.Add(target);
double mu = myTimeSeriForBestHybrid.part1.Average();
if (myBestD == 0)
{
Zt.Add(myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 1] - mu);
}
else if (myBestD == 1)
{
Zt.Add(myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 1] -
myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 2] - mu);
}
else //else if (bestD == 2) << CHECK HERE >>
{
Zt.Add(myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 1] -
2 * myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 2] +
myTimeSeriForBestHybrid.part1[myTimeSeriForBestHybrid.part1.Count - 3] - mu);
}
}
double _mse = MyErrorParameters.MSE(results.ToArray(), myTimeSeriForBestHybrid.testCases.ToArray());
double _errorPercent = MyErrorParameters.ERROR_Percent(results.ToArray(), myTimeSeriForBestHybrid.testCases.ToArray());
hybridWriter.WriteLine("\n\n\nMSE & ERROR% are =>\n\n{0} {1}", _mse, _errorPercent);
hybridWriter.Flush();
hybridWriter.Close();
MessageBox.Show(
String.Format(
"Complex Hybrid Model Created File {0} For Output Successfully Now , Please Check It Out .",
OUTPUT_FILE_NAME), "Hybrid SVM Arima Done", MessageBoxButton.OK,
MessageBoxImage.Information);
}
