public ProductForCast getProductSalesPredictions(int productID, int numPredictions, int value1 = 1, int value2 = 5)
{
var toreturn = new ProductForCast();
var pastSales = (from c in db.productsalespermonths where c.Product_ID == productID select c.sales.Value).ToList();
toreturn.productID = productID;
toreturn.name = db.products.Find(productID).productName;
toreturn.previouse = Array.ConvertAll(pastSales.ToArray(), x => (double)x);
ArimaModel model = new ArimaModel(toreturn.previouse, value1, value2);
model.Compute();
toreturn.predictions = Array.ConvertAll(model.Forecast(numPredictions).ToArray(), x => (double)x);
return(toreturn);
}
public static void Optimize()
{
SARIMAForm bpf = new SARIMAForm();
if (bpf.ShowDialog() != DialogResult.OK)
{
return;
}
SelectData(bpf.dateTimePicker1.Value, bpf.dateTimePicker2.Value, int.Parse(bpf.tbM.Text));
NumericalVariable traf = new NumericalVariable("Трафик", arrTraf);
ArimaModel model = new ArimaModel(traf, int.Parse(bpf.tbAR.Text), int.Parse(bpf.tbD.Text), int.Parse(bpf.tbMA.Text));
//model.Compute();
List <string> list = new List <string>();
list.Add("Параметры модели авторегрессии:");
//for (int i = 0; i < model.AutoRegressiveParameters.Count; i++)
//list.Add(string.Format("{0} = {1:g4}", model.AutoRegressiveParameters[i].Name, model.AutoRegressiveParameters[i].PValue));
//list.Add("Параметры модели скользящего среднего:");
//for (int i = 0; i < model.MovingAverageParameters.Count; i++)
//list.Add(string.Format("{0} = {1:g4}", model.MovingAverageParameters[i].Name, model.MovingAverageParameters[i].PValue));
//list.Add(string.Format("Среднее: {0:g4}", model.Mean));
int p = int.Parse(bpf.tbProg.Text);
List <double> lF = new List <double>();
TimeSpan ts = arrDT.Last() - arrDT[arrDT.Length - 2], ts2 = new TimeSpan(ts.Ticks / 2);
DateTime dt = arrDT.Last() + ts;
double av = arrTraf.Average();
for (int i = 0; i < p; i++)
{
lF.Add(prInt(dt - ts2, dt + ts2));
dt += ts;
}
arrF = lF.ToArray();
double avF = arrF.Average();
for (int i = 0; i < arrF.Length; i++)
{
arrF[i] /= avF / av;
}
//arrF = model.Forecast(p).ToArray();
ModelForm mf = new ModelForm(list.ToArray());
mf.ShowDialog();
}
public double[][] Forecast(int days)
{
var openVector = Vector.Create(openValues);
var closeVector = Vector.Create(closeValues);
ArimaModel openModel = new ArimaModel(openVector, parameters[0], parameters[1], parameters[2]);
ArimaModel closeModel = new ArimaModel(openVector, parameters[0], parameters[1], parameters[2]);
openModel.Fit();
closeModel.Fit();
double[][] forecast = new double[2][];
forecast[0] = openModel.Forecast(days).ToArray(); // open forecast
forecast[1] = closeModel.Forecast(days).ToArray(); // close forecast
return(forecast);
}
public double[] ArimaTest(int lag)
{
// The time series data is stored in a numerical variable:
var sunspots = Vector.Create(100.8, 81.6, 66.5, 34.8, 30.6, 7, 19.8, 92.5,
154.4, 125.9, 84.8, 68.1, 38.5, 22.8, 10.2, 24.1, 82.9, 132, 130.9, 118.1, 89.9, 66.6, 60, 46.9, 41,
21.3, 16, 6.4, 4.1, 6.8, 14.5, 34, 45, 43.1, 47.5, 42.2, 28.1, 10.1, 8.1, 2.5, 0, 1.4, 5, 12.2, 13.9,
35.4, 45.8, 41.1, 30.4, 23.9, 15.7, 6.6, 4, 1.8, 8.5, 16.6, 36.3, 49.7, 62.5, 67, 71, 47.8, 27.5, 8.5,
13.2, 56.9, 121.5, 138.3, 103.2, 85.8, 63.2, 36.8, 24.2, 10.7, 15, 40.1, 61.5, 98.5, 124.3, 95.9, 66.5,
64.5, 54.2, 39, 20.6, 6.7, 4.3, 22.8, 54.8, 93.8, 95.7, 77.2, 59.1, 44, 47, 30.5, 16.3, 7.3, 37.3,
73.9);
//Find 'd'.
AugmentedDickeyFullerTest adf = new AugmentedDickeyFullerTest(sunspots, 0);
double d = adf.Statistic;
// Console.WriteLine(d);
// Console.WriteLine(adf.PValue);
// An integrated model (with differencing) is constructed
// by supplying the degree of differencing. Note the order
// of the orders is the traditional one for an ARIMA(p,d,q)
// model (p, d, q).
// The following constructs an ARIMA(0,1,1) model:
ArimaModel model = new ArimaModel(sunspots, 2, 1);
model.EstimateMean = true;
// The Compute methods fits the model.
model.Fit();
//Predict a specified number of values:
var nextValues = model.Forecast(5);
//Cast to double[].
var castedNextValues = new double[nextValues.Length];
for (var i = 0; i < nextValues.Length; i++)
{
castedNextValues[i] = nextValues[i];
}
return(castedNextValues);
}
public double[] Arima(int forecast, double[] values)
{
Vector <double> tempValues = Vector.Create(values);
//Find 'd'.
var adf = new AugmentedDickeyFullerTest(values, 0);
var p = adf.PValue;
var acf = tempValues.AutocorrelationFunction(20);
if (p > 0.05D)
{
p = 1;
}
tempValues = tempValues.Difference();
// An integrated model (with differencing) is constructed
// by supplying the degree of differencing. Note the order
// of the orders is the traditional one for an ARIMA(p,d,q)
// model (p, d, q).
var model = new ArimaModel(values, (int)p, 3, 3);
model.EstimateMean = true;
// The Compute methods fits the model.
model.Fit();
//Predict a specified number of values:
var nextValues = model.Forecast(forecast);
//Cast to double[].
var castedNextValues = new double[nextValues.Length];
for (var i = 0; i < nextValues.Length; i++)
{
castedNextValues[i] = nextValues[i];
}
// return castedNextValues;
return(castedNextValues);
}
} // pure static class
/// <summary>
/// Raw-level ARIMA forecasting function.
/// </summary>
/// <param name="data"> UNMODIFIED, list of double numbers representing time-series with constant time-gap </param>
/// <param name="forecastSize"> integer representing how many data points AFTER the data series to be
/// forecasted </param>
/// <param name="params"> ARIMA parameters </param>
/// <returns> a ForecastResult object, which contains the forecasted values and/or error message(s) </returns>
//JAVA TO C# CONVERTER WARNING: 'final' parameters are not available in .NET:
//ORIGINAL LINE: public static TimeSeries.Forecast.TimeSeries.Arima.struct.ForecastResult forecast_arima(final double[] data, final int forecastSize, TimeSeries.Forecast.TimeSeries.Arima.struct.ArimaParams params)
public static ForecastResult forecast_arima(double[] data, int forecastSize, ArimaParams @params)
{
try
{
int p = @params.p;
int d = @params.d;
int q = @params.q;
int P = @params.P;
int D = @params.D;
int Q = @params.Q;
int m = @params.m;
ArimaParams paramsForecast = new ArimaParams(p, d, q, P, D, Q, m);
ArimaParams paramsXValidation = new ArimaParams(p, d, q, P, D, Q, m);
// estimate ARIMA model parameters for forecasting
ArimaModel fittedModel = ArimaSolver.estimateARIMA(paramsForecast, data, data.Length, data.Length + 1);
// compute RMSE to be used in confidence interval computation
double rmseValidation = ArimaSolver.computeRMSEValidation(data, ForecastUtil.testSetPercentage, paramsXValidation);
fittedModel.RMSE = rmseValidation;
ForecastResult forecastResult = fittedModel.Forecast(forecastSize);
// populate confidence interval
forecastResult.Sigma2AndPredicationInterval = fittedModel.Params;
// add logging messages
forecastResult.Log("{" + "\"Best ModelInterface Param\" : \"" + fittedModel.Params.summary() + "\"," + "\"Forecast Size\" : \"" + forecastSize + "\"," + "\"Input Size\" : \"" + data.Length + "\"" + "}");
// successfully built ARIMA model and its forecast
return(forecastResult);
}
catch (Exception ex)
{
// failed to build ARIMA model
throw new Exception("Failed to build ARIMA forecast: " + ex.Message);
}
}
static void Main(string[] args)
{
//require R 2.15, package forecast on R
var envPath = Environment.GetEnvironmentVariable("PATH");
var rBinPath = GetRPath(); //C:\Program Files\R\R-2.15.1\bin\i386
Environment.SetEnvironmentVariable("PATH", envPath + Path.PathSeparator + rBinPath);
REngine engine = REngine.CreateInstance("RDotNet");
engine.Initialize();
string currentPath = Directory.GetCurrentDirectory();
string dataPath = currentPath + @"\data\paper.dat";
string readDataCommand = string.Format("predata <- read.table(\"{0}\", header=FALSE)", dataPath).Replace('\\', '/');
engine.Evaluate("library(forecast)");
engine.Evaluate(readDataCommand);
engine.Evaluate("data <- predata[,1]");
var model = engine.Evaluate("fit <- auto.arima(data)").AsList();
var coef = model["coef"].AsList();
int lengthData = engine.Evaluate("data").AsNumeric().Length;
double[] dataSeries = new double[lengthData];
double[] errorSeries = new double[lengthData];
engine.Evaluate("data").AsNumeric().CopyTo(dataSeries, lengthData);
model["residuals"].AsNumeric().CopyTo(errorSeries, lengthData);
//residuals
int arOrder = model["arma"].AsInteger().ElementAt(0);
int maOrder = model["arma"].AsInteger().ElementAt(1);
int arSeasonOrder = model["arma"].AsInteger().ElementAt(2);
int maSeasonOrder = model["arma"].AsInteger().ElementAt(3);
int seasonOrder = model["arma"].AsInteger().ElementAt(4);
int diffOrder = model["arma"].AsInteger().ElementAt(5);
int diffSeasonOrder = model["arma"].AsInteger().ElementAt(6);
double[] arCoef = new double[arOrder];
double[] maCoef = new double[maOrder];
double[] arSeasonCoef = new double[arSeasonOrder];
double[] maSeasonCoef = new double[maSeasonOrder];
double intercept = 0;
int n = model["coef"].AsNumeric().Length;
int start = 0;
model["coef"].AsNumeric().CopyTo(arCoef, arOrder, start, 0);
start += arOrder;
model["coef"].AsNumeric().CopyTo(maCoef, maOrder, start, 0);
start += maOrder;
model["coef"].AsNumeric().CopyTo(arSeasonCoef, arSeasonOrder, start, 0);
start += arSeasonOrder;
model["coef"].AsNumeric().CopyTo(maSeasonCoef, maSeasonOrder, start, 0);
start += maSeasonOrder;
if (n > start)
{
intercept = model["coef"].AsNumeric().ElementAt(start);
}
ArimaModel arimaModel = new ArimaModel(arCoef, maCoef, arSeasonCoef, maSeasonCoef, intercept, (uint)seasonOrder, (uint)diffOrder, (uint)diffSeasonOrder);
Polynomial arModel = arimaModel.ComputeARModel();
Polynomial maModel = arimaModel.ComputeMAModel();
double interceptModel = arimaModel.ComputeIntercept();
double test = arimaModel.ComputeValue(dataSeries, errorSeries, dataSeries.Length);
Console.WriteLine("Forecast");
Console.WriteLine(test);
Console.WriteLine("Model");
Console.WriteLine(interceptModel);
Console.WriteLine("Ar");
Console.WriteLine(arModel.ToString());
Console.WriteLine("Ma");
Console.WriteLine(maModel.ToString());
Console.ReadLine();
}
public void Test(HomeController controller)
{
//require R 2.15, package forecast on R
//var envPath = Environment.GetEnvironmentVariable("PATH");
//var rBinPath = GetRPath(); //C:\Program Files\R\R-2.15.1\bin\i386
//var BinPath = GetWinRegistryPath();
//var envPath = Environment.GetEnvironmentVariable("PATH");
// this statement not work under iis
//Environment.SetEnvironmentVariable("PATH", envPath + Path.PathSeparator + BinPath);
REngine engine = REngine.GetInstance();
engine.Initialize();
string currentPath = @"C:\Workspace\SAM\SAM\VisualAnalytics\bin"; //Directory.GetCurrentDirectory();
string dataPath = currentPath + @"\data\paper.dat";
string readDataCommand = string.Format("predata <- read.table(\"{0}\", header=FALSE)", dataPath).Replace('\\', '/');
//engine.Evaluate("install.packages(\"forecast\")");
engine.Evaluate("library(jsonlite)");
engine.Evaluate("library(forecast)");
String JsonData = @"http://localhost:25910/Home/getBigTable";//controller.getBigTable().Content;
log.Debug("JSON:" + JsonData);
string evaluate = string.Format("data <-fromJSON(\"{0}\")", JsonData);
log.Debug("evaluateString:" + evaluate);
engine.Evaluate(evaluate);
//log.Debug(engine.Evaluate("data"));
//engine.Evaluate("data <- predata[,1]");
var model = engine.Evaluate("fit <- auto.arima(data$Amount)").AsList();
foreach (var item in model)
{
log.Debug(item.ToString());
}
var coef = model["coef"].AsList();
int lengthData = engine.Evaluate("data$Amount").AsNumeric().Length;
double[] dataSeries = new double[lengthData];
double[] errorSeries = new double[lengthData];
engine.Evaluate("data$Amount").AsNumeric().CopyTo(dataSeries, lengthData);
model["residuals"].AsNumeric().CopyTo(errorSeries, lengthData);
//residuals
int arOrder = model["arma"].AsInteger().ElementAt(0);
int maOrder = model["arma"].AsInteger().ElementAt(1);
int arSeasonOrder = model["arma"].AsInteger().ElementAt(2);
int maSeasonOrder = model["arma"].AsInteger().ElementAt(3);
int seasonOrder = model["arma"].AsInteger().ElementAt(4);
int diffOrder = model["arma"].AsInteger().ElementAt(5);
int diffSeasonOrder = model["arma"].AsInteger().ElementAt(6);
double[] arCoef = new double[arOrder];
double[] maCoef = new double[maOrder];
double[] arSeasonCoef = new double[arSeasonOrder];
double[] maSeasonCoef = new double[maSeasonOrder];
double intercept = 0;
int n = model["coef"].AsNumeric().Length;
int start = 0;
model["coef"].AsNumeric().CopyTo(arCoef, arOrder, start, 0);
start += arOrder;
model["coef"].AsNumeric().CopyTo(maCoef, maOrder, start, 0);
start += maOrder;
model["coef"].AsNumeric().CopyTo(arSeasonCoef, arSeasonOrder, start, 0);
start += arSeasonOrder;
model["coef"].AsNumeric().CopyTo(maSeasonCoef, maSeasonOrder, start, 0);
start += maSeasonOrder;
if (n > start)
{
intercept = model["coef"].AsNumeric().ElementAt(start);
}
ArimaModel arimaModel = new ArimaModel(arCoef, maCoef, arSeasonCoef, maSeasonCoef, intercept, (uint)seasonOrder, (uint)diffOrder, (uint)diffSeasonOrder);
Polynomial arModel = arimaModel.ComputeARModel();
Polynomial maModel = arimaModel.ComputeMAModel();
double interceptModel = arimaModel.ComputeIntercept();
double test = arimaModel.ComputeValue(dataSeries, errorSeries, dataSeries.Length);
log.Info("Forecast");
log.Info(test);
log.Info("Model");
log.Info(interceptModel);
log.Info("Ar");
log.Info(arModel.ToString());
log.Info("Ma");
log.Info(maModel.ToString());
//Console.ReadLine();
}
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);
}
public static async Task <ScalingState> ScalingFunction(
[OrchestrationTrigger] DurableOrchestrationContext context,
ILogger log)
{
log.LogScalingFunction("Get new input.");
var scalingState = context.GetInput <ScalingState>();
log.LogScalingFunction($"Scaling for {scalingState.CurrentPartOfPeriod} part of period.");
log.LogScalingFunction("Waiting for event from Metrics Collection");
if (scalingState.Wait)
{
var @event = await context.WaitForExternalEvent <MetricCollected>(nameof(MetricCollected));
log.LogScalingFunction($"Update current metrics data.");
scalingState.AddMetric(@event.Metric);
}
log.LogScalingFunction($"Prepare ARIMA model. Autoregresive: {AutoregresiveParam}, Integration: {IntegrationParam}, Move Averrage: {MoveAverrageParam}");
var vectorMetricsData = Vector.Create(scalingState.MetricData.ToArray());
var model = new ArimaModel(vectorMetricsData, AutoregresiveParam, IntegrationParam, MoveAverrageParam);
model.EstimateMean = true;
log.LogScalingFunction("Fit ARIMA model.");
model.Fit();
log.LogScalingFunction("Forecast capacity for rest period.");
var period = MasterPerformMetricsCollector.Period - scalingState.CurrentPartOfPeriod - 1;
var forecastedData = model.Forecast(period);
log.LogScalingFunction("Calculate capacity");
var capacityPerDay = forecastedData.Select(z => CapacityHelpers.CalculateCapacity(z)).ToList();
var cost = capacityPerDay.Select(z => CapacityHelpers.CalculateCostOfCapacity(z)).Sum();
var restCost = scalingState.RestCost - cost;
var division = (int)restCost / period;
if (division >= 1)
{
var additionalMachine = division > 2 ? division / MasterPerformMetricsCollector.Q : 1;
capacityPerDay = capacityPerDay.Select(z => z + additionalMachine).ToList();
restCost -= (additionalMachine * capacityPerDay.Count);
}
log.LogScalingFunction("Get capacity for part of period.");
var capacity = capacityPerDay.First();
log.LogScalingFunction($"Capacity to set: {capacity}");
var action = new ScaleAction(capacity);
await context.CallActivityAsync(nameof(Scaler), action);
log.LogScalingFunction("Start new Scaling Function with new scaling state.");
scalingState.NextPart(CapacityHelpers.CalculateCostOfCapacity(capacity));
context.ContinueAsNew(scalingState);
return(scalingState);
}