//Methods
/// <summary>
/// Adds the next value into the stored time series.
/// </summary>
/// <param name="nextValue">The next value to be added.</param>
public void AddNextValue(double nextValue)
{
//Add new value
_valueCollection.Add(nextValue);
//Affect next value to existing averages
Parallel.For(0, _subIntervalLengthCollection.Count, _subIntervalIdx =>
{
int intervalLength = _subIntervalLengthCollection[_subIntervalIdx];
RescaledRange intervalRescaledRange = new RescaledRange(intervalLength);
for (int valueIdx = (_valueCollection.Count - intervalLength); valueIdx < _valueCollection.Count; valueIdx++)
{
intervalRescaledRange.AddValue(_valueCollection[valueIdx]);
}
_avgCollection[_subIntervalIdx].AddSample(intervalRescaledRange.Compute());
});
return;
}
/// <summary>
/// Creates an initialized instance.
/// </summary>
/// <param name="timeSeries">The time series data.</param>
/// <param name="subIntervalLengthCollection">The collection of the lengths of the rescaled range intervals.</param>
public HurstExpEstim(IEnumerable <double> timeSeries, List <int> subIntervalLengthCollection)
{
_valueCollection = timeSeries.ToList();
//Check the time series length
if (_valueCollection.Count < MinSubIntervalLength + 1)
{
throw new ArgumentException($"Time series is too short. Minimal length is {MinSubIntervalLength + 1}", "timeSeries");
}
//Subintervals
if (subIntervalLengthCollection != null)
{
_subIntervalLengthCollection = new List <int>(subIntervalLengthCollection);
}
else
{
_subIntervalLengthCollection = new List <int>((_valueCollection.Count - MinSubIntervalLength) + 1);
for (int i = 0, length = MinSubIntervalLength; length <= _valueCollection.Count; i++, length++)
{
_subIntervalLengthCollection.Add(length);
}
}
_avgCollection = new List <WeightedAvg>(_subIntervalLengthCollection.Count);
for (int i = 0; i < _subIntervalLengthCollection.Count; i++)
{
_avgCollection.Add(new WeightedAvg());
}
Parallel.For(0, _subIntervalLengthCollection.Count, _subIntervalIdx =>
{
int intervalLength = _subIntervalLengthCollection[_subIntervalIdx];
RescaledRange rescaledRange = new RescaledRange(intervalLength);
for (int startIdx = 0; startIdx <= _valueCollection.Count - intervalLength; startIdx++)
{
rescaledRange.Reset();
for (int valueSubIdx = 0, timeSeriesIdx = startIdx; valueSubIdx < intervalLength; valueSubIdx++, timeSeriesIdx++)
{
rescaledRange.AddValue(_valueCollection[timeSeriesIdx]);
}
_avgCollection[_subIntervalIdx].AddSample(rescaledRange.Compute());
}
});
return;
}
/// <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);
}