/// <summary>
/// This function uses the Durbin-Levinson algorithm
/// to get one-step predictors and residuals.
/// </summary>
/// <param name="rs">double[] array of one-step predictive MSEs / sigma^2,
// based on sigma when this routine was called </param>
/// <returns>double[] array of residuals = [ (x_i - \hat{x}_i)/\sqrt{rs_i} ]</returns>
protected double[] ComputeSpecialResiduals(TimeSeries startData, out double[] rs, int forecastHorizon,
out double[] forecasts)
{
// 1. residuals are returned: retval[] = [ (x_i - \hat{x}_i)/\sqrt{rs_{i-1}} ]
// 2. rs[...] are one-step predictive MSEs / sigma^2,
// based on sigma when this routine was called
int i, n, nobs = startData.Count + forecastHorizon;
var xhat = Vector <double> .Build.Dense(Math.Max(nobs + 1, 1));
var localResiduals = new double[nobs];
rs = new double[nobs + 1];
var dl = new DurbinLevinsonPredictor(Mu, Autocovariance, nobs + 1);
xhat[0] = dl.CurrentPredictor;
rs[0] = dl.CurrentMSPE;
for (i = 1; i <= nobs; ++i)
{
if (i <= startData.Count)
{
dl.Register(startData[i - 1]);
}
else
{
dl.Register(xhat[i - 1]);
}
xhat[i] = dl.CurrentPredictor;
rs[i] = dl.CurrentMSPE;
}
for (i = 0; i < rs.Length; ++i)
{
rs[i] /= (Sigma * Sigma);
}
// store the results
for (n = 0; n < startData.Count; ++n)
{
localResiduals[n] = (startData[n] - xhat[n]) / Math.Sqrt(rs[n]);
}
if (forecastHorizon > 0)
{
forecasts = new double[forecastHorizon];
for (n = startData.Count; n < nobs; ++n)
{
forecasts[n - startData.Count] = xhat[n];
}
}
else
{
forecasts = null;
}
return(localResiduals);
}
public virtual void ResetRealTimePrediction()
{
realTimePredictor = new DurbinLevinsonPredictor(Mu, Autocovariance, 20000);
}
