public EstimationResult Estimate(IEnumerable <IDateValue> dateValues)
{
var series = new TimeSeries();
dateValues.ForEach(x => series.Add(x.Date, x.Value, true));
armaModel.SetInput(0, series, null);
armaModel.FitByMLE(200, 100, 0, null);
armaModel.ComputeResidualsAndOutputs();
var result = armaModel.GetOutput(3) as TimeSeries;
return(EstimationResult.Create(result[0], this));
}
protected override TimeSeries _BuildOutput(TimeSeries simulatedData, object userState = null)
{
ARMAModel model = new ARMAModel(mAROrder, mMAOrder);
model.SetInput(0, simulatedData, null);
//Maximum Likelihood Estimation
model.FitByMLE(mNumberIterLDS, mNumberIterOpt, 0, null); // first param is # low discrepancy sequence iterations, should be at least about 200
// second param is # standard optimizer iterations, should be at least about 100
//Compute the residuals
model.ComputeResidualsAndOutputs();
//model1.GetOutputName(3);
//model1.Description;
//Get the predicted values
return(model.GetOutput(3) as TimeSeries);
}