 | Moving |
The MovingWindowFilter type exposes the following members.
Methods| Name | Description | |
|---|---|---|
 | Correlate |
Does the correlation and takes care of internal correlation and work objects.
(Inherited from CorrelationFilter) |
   | ExponentiallyWeightedMovingAverageCoefficients | Returns a vector of exponentially weighted moving average (EWMA) coefficients of length n. |
| Filter(DoubleVector) |
Filters C# data (Overrides CorrelationFilterFilter(DoubleVector)) |
| Filter(DoubleVector, DoubleVector) |
Filters C# data (Overrides CorrelationFilterFilter(DoubleVector, DoubleVector)) |
| Filter(DoubleVector, MovingWindowFilterBoundaryOption) | Applies the filter to the given data using the given boundary option. The given boundary option sets the current boundary option. |
| Filter(DoubleVector, MovingWindowFilterBoundaryOption, DoubleVector) | Applies the filter to the given data using the given boundary option and places the output in a given vector. The given boundary options sets the current boundary option. |
| MovingAverageCoefficients | Constructs the coefficient vector that implements a moving average filter when used with the MovingWindowFilter class. |
| SavitzkyGolayCoefficients | Constructs the coefficient vector that implements a Savitzky-Golay smoothing filter when used with the MovingWindowFilter class. The algorithm is also known by the terms, least-squares, or DIgital Smoothing POlynomial (DISPO). The filter coefficients, c(n) are chosen so as to approximate the underlying function in the window [i - nL, i + nR] with a polynomial, typically quadratic or quartic, and replace the point f(i) with the value of the approximating polynomial at i. The polynomial is fit using a least squares algorithm. |
| SetFilterParameters | Sets the parameters for this filter. |
See Also