public override TimeSeries BuildForecasts(TimeSeries simulatedData, List <DateTime> futureTimes)
{
// fit model first, using maximum likelihood estimation
var model = new ARMAModel(mAROrder, mMAOrder); // create the model object
model.TheData = simulatedData; // this is the data we want to fit the model to
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
// now do some forecasting beyond the end of the data
var forecaster = new ForecastTransform();
forecaster.FutureTimes = futureTimes.ToArray(); // these are future times at which we want forecasts
// for now, ARMA models do not use the times in any meaningful way when forecasting,
// they just assume that these are the timestamps for the next sequential points
// after the end of the existing time series
forecaster.SetInput(0, model, null); // the ARMA model used for forecasting
forecaster.SetInput(1, simulatedData, null); // the original data
// normally you would call the Recompute() method of a transform, but there is no need
// here; it is automatically called as soon as all inputs are set to valid values (in the 2 statements above)
var predictors = forecaster.GetOutput(0) as TimeSeries;
return(predictors);
}
// forecasts values for the given dates from the model
public TimeSeries predictARMA(List <DateTime> futureTimes, ARMAModel model)
{
var forecaster = new ForecastTransform();
forecaster.FutureTimes = futureTimes.ToArray();
forecaster.SetInput(0, model, null);
forecaster.SetInput(1, model.TheData, null);
var predictors = forecaster.GetOutput(0) as TimeSeries;
// now we need to merge the forecast time series and the original time series with the original data
var merger = new MergeTransform();
merger.SetInput(0, model.TheData, null);
merger.SetInput(1, predictors, null);
merger.Recompute();
return(merger.GetOutput(0) as TimeSeries);
}
