The ARMAMaxLikelihood type exposes the following members.

Constructors

NameDescription
ARMAMaxLikelihood
Constructor for ARMAMaxLikelihood.

Methods

NameDescription
Compute
Computes the exact maximum likelihood estimates for the autoregressive and moving average parameters of an ARMA time series.
Equals
Determines whether the specified Object is equal to the current Object.
(Inherited from Object.)
Finalize
Allows an Object to attempt to free resources and perform other cleanup operations before the Object is reclaimed by garbage collection.
(Inherited from Object.)
Forecast
Returns forecasts for lead times l=1,2,\ldots,\rm{nForecast} at origins z.Length-BackwardOrigin-1+j where j=1,\ldots,\rm{BackwardOrigin}+1.
GetAR
Returns the final autoregressive parameter estimates.
GetDeviations
Returns the deviations for each forecast used for calculating the forecast confidence limits.
GetForecast
Returns forecasts
GetGradients
Returns the gradients for the final parameter estimates.
GetHashCode
Serves as a hash function for a particular type.
(Inherited from Object.)
GetMA
Returns the final moving average parameter estimates.
GetPsiWeights
Returns the psi weights used for calculating forecasts from the infinite order moving average form of the ARMA model.
GetResiduals
The current values of the vector of residuals.
GetTimeSeries
Returns the time series used to construct ARMAMaxLikelihood.
GetType
Gets the Type of the current instance.
(Inherited from Object.)
IsInvertible
Tests whether the coefficients in ma are invertible.
IsStationary
Tests whether the coefficients in ar are stationary.
MemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
SetAR
Sets the initial values for the autoregressive terms to the p values in ar.
SetMA
Sets the initial values for the moving average terms to the q values in ma.
ToString
Returns a String that represents the current Object.
(Inherited from Object.)

Properties

NameDescription
BackwardOrigin
The maximum backward origin.
Confidence
The confidence level for calculating confidence limit deviations returned from GetDeviations.
Constant
The constant parameter in the ARMA series.
GradientTolerance
The gradient tolerance for the convergence algorithm.
InnovationVariance
The estimated innovation variance of this series.
Likelihood
The final estimate for -2\ln(L), where L is equal to the likelihood function evaluated using the final parameter estimates.
MaxIterations
The maximum number of iterations.
Mean
The mean used for centering the series.
P
The number of autoregressive terms in the ARMA model
Q
The number of moving average terms in the ARMA model
Tolerance
The tolerance for the convergence algorithm.

See Also