The ARAutoUnivariate type exposes the following members.
Constructors
| Name | Description | |
|---|---|---|
 | ARAutoUnivariate |
ARAutoUnivariate constructor.
|
Methods
| Name | Description | |
|---|---|---|
| Compute |
Determines the autoregressive model with the minimum AIC by fitting
autoregressive models from 0 to maxlag lags using the method
of moments or an estimation method specified by the user through
EstimationMethod.
|
| Equals | (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 and associated confidence interval offsets.
|
| GetAR |
Returns the final autoregressive parameter estimates at the
optimum AIC using the estimation method specified in
EstimationMethod.
|
| GetDeviations |
Returns the deviations for each forecast used for calculating the
forecast confidence limits.
|
| GetForecast |
Returns a specified number of forecasts beyond the last value in the series.
|
| GetHashCode |
Serves as a hash function for a particular type.
(Inherited from Object.) |
| GetResiduals |
Returns the current values of the vector of residuals.
|
| GetTimeSeries |
Returns the time series used for estimating the minimum AIC and the
autoregressive coefficients.
|
| GetTimsacAR |
Returns the final auto regressive parameter estimates at the
optimum AIC estimated by the original TIMSAC routine (UNIMAR).
|
| GetType |
Gets the Type of the current instance.
(Inherited from Object.) |
| MemberwiseClone |
Creates a shallow copy of the current Object.
(Inherited from Object.) |
| ToString | (Inherited from Object.) |
Properties
| Name | Description | |
|---|---|---|
 | AIC |
The final estimate for Akaike's Information Criterion (AIC)
at the optimum.
|
| BackwardOrigin |
The maximum backward origin used in calculating the forecasts.
|
| Confidence |
The confidence level for calculating confidence limit deviations
returned from GetDeviations.
|
| Constant |
The estimate for the constant parameter in the ARMA series.
|
| ConvergenceTolerance |
The tolerance level used to determine convergence of the nonlinear
least-squares and maximum likelihood algorithms.
|
| EstimationMethod |
The estimation method used for estimating the final estimates for
the autoregressive coefficients.
|
| InnovationVariance |
The final estimate for the innovation variance.
|
| Likelihood |
The final estimate for  ,
where p is the AR order, AIC is the value of Akaike's
Information Criterion, and L is the likelihood function
evaluated for the optimum autoregressive model.
|
| MaxIterations |
The maximum number of iterations used for estimating the
autoregressive coefficients.
|
| Maxlag |
The current value used to represent the maximum number of
autoregressive lags to achieve the minimum AIC.
|
| Mean |
The mean used to center the time series z.
|
| NumberOfProcessors |
Perform the parallel calculations with the maximum possible number of
processors set to NumberOfProcessors.
|
| Order |
The order of the AR model selected with the minimum AIC.
|
| TimsacConstant |
The estimate for the constant parameter in the ARMA series.
|
| TimsacVariance |
The final estimate for the innovation variance calculated
by the TIMSAC automatic AR modeling routine (UNIMAR).
|
