	Name	Description
	DynamicParameterEstimation(Int32, Int32, Int32)	Constructor for the dynamic parameter estimation.
	DynamicParameterEstimation(Int32, Int32, Int32, IDynamicParameterEstimationSolver)	Constructor for the dynamic parameter estimation.
	DynamicParameterEstimation(IReadOnlyListDouble, IReadOnlyListDouble, Int32, Int32, Int32)	Calculates the dynamic parameter estimation in the constructor.

	Name	Description
	LUSolver	Gets a fresh instance of an LU decomposition solver that can be used for parameter estimation.
	OffsetX	Gets or sets the offset that "moves" the sequence x in relation to sequence y. Normally, y[i] is considered in dependence on x[i], x[i-1], and so on. By setting the offset, y[i] is considered in dependence on x[i-offset], x[i-offset-1], etc.
	Parameter	Gets the resulting parameters of the estimation. Index 0..numX-1 are the parameters for x history. Following from numX to numX+numY-1 are the parameters for y. Finally, the remaining parameters are the parameters for the background fit.
	QRSolver	Gets a fresh instance of a QR decomposition solver that can be used for parameter estimation.
	StartingPoint	Gets the starting point, i.e. the first index in the y array that can be used for the right side of the linear equation. The starting point increases when more x or y parameters are evaluated, since more "history" samples are needed in this case.
	SVDSolver	Gets a fresh instance of a singular value decomposition solver that can be used for parameter estimation.

	Name	Description
	CalculateCrossPredictionError(IReadOnlyListDouble, IReadOnlyListDouble)	With the already evaluated parameters, calculates the mean error for another piece of data. Note that both vectors must have a length of at least _startingPoint + 1, since the first _startingPoint samples are used for the history.
	CalculateCrossPredictionError(IReadOnlyListDouble, IReadOnlyListDouble, IVectorDouble)	With the already evaluated parameters, calculates the mean error for another piece of data. Note that both vectors must have a length of at least _startingPoint + 1, since the first _startingPoint samples are used for the history.
	CalculateNumberOfData	Calculates the number of points that can be used on the right side of the linear equation (i.e. the number of rows of the equation). With the same length of x and y, the number of usable data points decreases when more x or y parameters are evaluated, since more samples are needed for the history and those samples cannot be used on the right side of the equation.
	CalculatePredictionError	Calculates the mean prediction error, i.e. Sqrt(Sum((y-yPredicted)²)/N).
	CalculatePredictionError(VectorDouble)	Calculates the mean prediction error, i.e. Sqrt(Sum((y-yPredicted)²)/N).
	CalculateResultingParameter	Calculates the resulting parameter array by calling the solver.
	CalculateSelfPredictionError	Calculates the mean prediction error using recursive prediction of y (self-prediction).
	CalculateSelfPredictionError(IVectorDouble)	Calculates the mean prediction error using recursive prediction of y (self-prediction).
	CalculateSelfPredictionError(MatrixDouble, IReadOnlyListDouble, IVectorDouble)	Calculates the mean prediction error using recursive prediction of y (self-prediction) for the given matrix and comparison y values.
	CalculateStartingPoint	Calculates the starting point, i.e. the first index in the y array that can be used for the right side of the linear equation. The starting point increases when more x or y parameters are evaluated, since more "history" samples are needed in this case.
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object)
	EstimateParameterByBurgsAlgorithm	Estimates the parameters using Burg's algorithm.
	Extrapolate	Extrapolates y-values until the end of the vector by using linear prediction.
	FillBacksubstitutionY	Fills the back-substitution array with data from the provided y vector.
	FillInputMatrix	Fills the input matrix, i.e. the left side of the linear equation.
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object)
	GetFrequencyResponse	Calculates the frequency response for a given frequency.
	GetHashCode	Serves as the default hash function. (Inherited from Object)
	GetTransferFunction	Gets the impulse response to a pulse at t=0, i.e. to x[0]==1, x[1]..x[n]==0. The background component is not taken into account.
	GetType	Gets the Type of the current instance. (Inherited from Object)
	MakeEstimation	Calculates the dynamic parameter estimation.
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object)
	SetHelperMembers	Sets all helper values such as _numX, _numY, _backgroundOrderPlus1, _numberOfParameter, and _startingPoint.
	ToString	Returns a string that represents the current object. (Inherited from Object)

	Name	Description
	_backgroundOrderPlus1	Number of background parameters to estimate.
	_inputMatrix	Holds the input matrix.
	_numberOfParameter	Total number of parameters, i.e. _numX + _numY + _backgroundOrderPlus1.
	_numX	Number of x parameters to estimate.
	_numY	Number of y parameters to estimate.
	_offsetX	"Moves" the sequence x in relation to sequence y. Normally, y[i] is considered in dependence on x[i], x[i-1], and so on. By setting the offset, y[i] is considered in dependence on x[i-offset], x[i-offset-1], etc.
	_parameter	Array to store the estimated parameters. First in the array, the x parameters are stored (indices 0.._numX-1). Then the y parameters (indices _numX.._numX+_numY-1). Lastly, the background parameters are stored in the array (indices _numX+_numY..end).
	_scaledY	Array of y-values necessary for back-substitution. This is a copy of the input y vector only for the elements _startingPoint..end.
	_solver	Stores an instance of a solver used to solve the linear equation. The solver should keep and recycle the memory necessary for solving the equation.
	_startingPoint	Index of the point where the calculation can start.

DynamicParameterEstimation Class

Reference