The ARAutoUnivariate type exposes the following members.
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).
|
