/// <summary>
/// Estimates the Hurst Exponent, considering the specified hypothetical next value of the already stored time series.
/// </summary>
/// <remarks>
/// Operation does not change the instance data.
/// </remarks>
/// <returns>The resulting linear fit object.</returns>
public LinearFit ComputeNext(double simValue)
{
//Affect the simulated next value into the existing averages
double[] avgValues = new double[_avgCollection.Count];
Parallel.For(0, _subIntervalLengthCollection.Count, _subIntervalIdx =>
{
int intervalLength = _subIntervalLengthCollection[_subIntervalIdx];
RescaledRange intervalRescaledRange = new RescaledRange(intervalLength);
for (int valueIdx = (_valueCollection.Count - intervalLength) + 1; valueIdx < _valueCollection.Count; valueIdx++)
{
intervalRescaledRange.AddValue(_valueCollection[valueIdx]);
}
intervalRescaledRange.AddValue(simValue);
avgValues[_subIntervalIdx] = _avgCollection[_subIntervalIdx].SimulateNext(intervalRescaledRange.Compute());
});
//Add updated existing points
LinearFit linFit = new LinearFit();
for (int i = 0; i < _avgCollection.Count; i++)
{
double x = Math.Log(_subIntervalLengthCollection[i]);
double avg = avgValues[i];
double y = 0;
if (avg != 0)
{
y = Math.Log(avg);
}
linFit.AddSamplePoint(x, y);
}
//Return
return(linFit);
}
/// <summary>
/// Computes Hurst exponent estimation for next hypothetical value in time series.
/// Function does not change the instance, it is a simulation only.
/// </summary>
/// <param name="simValue">Next time series value</param>
public double ComputeNext(double simValue)
{
//Affect new value to existing averages
double[] avgValues = new double[_avgCollection.Count];
Parallel.For(0, _subIntervalLengthCollection.Count, _subIntervalIdx =>
{
int intervalLength = _subIntervalLengthCollection[_subIntervalIdx];
RescalledRange intervalRescalledRange = new RescalledRange(intervalLength);
for (int valueIdx = (_valueCollection.Count - intervalLength) + 1; valueIdx < _valueCollection.Count; valueIdx++)
{
intervalRescalledRange.AddValue(_valueCollection[valueIdx]);
}
intervalRescalledRange.AddValue(simValue);
avgValues[_subIntervalIdx] = _avgCollection[_subIntervalIdx].SimulateNext(intervalRescalledRange.Compute());
});
//Add updated existing points
LinearFit linFit = new LinearFit();
for (int i = 0; i < _avgCollection.Count; i++)
{
double x = Math.Log(_subIntervalLengthCollection[i]);
double avg = avgValues[i];
double y = 0;
if (avg != 0)
{
y = Math.Log(avg);
}
linFit.AddSamplePoint(x, y);
}
//Add new point for sub-interval length = (_timeSeries.Length + 1)
RescalledRange fullRescalledRange = new RescalledRange(_valueCollection.Count + 1);
foreach (double value in _valueCollection)
{
fullRescalledRange.AddValue(value);
}
fullRescalledRange.AddValue(simValue);
double fullRangeAvg = fullRescalledRange.Compute();
linFit.AddSamplePoint(Math.Log(_valueCollection.Count + 1), fullRangeAvg == 0 ? 0 : Math.Log(fullRangeAvg));
//Return
return(linFit.A);
}
/// <summary>
/// Estimates the Hurst Exponent.
/// </summary>
/// <returns>The resulting linear fit object</returns>
public LinearFit Compute()
{
LinearFit linFit = new LinearFit();
for (int i = 0; i < _avgCollection.Count; i++)
{
double x = Math.Log(_subIntervalLengthCollection[i]);
double avg = _avgCollection[i].Result;
double y = 0;
if (avg != 0)
{
y = Math.Log(avg);
}
linFit.AddSamplePoint(x, y);
}
return(linFit);
}