ARSeasonalFitGetTransformedTimeSeries Method |
Namespace: Imsl.Stat
Exception | Condition |
---|---|
MatrixSingularException | is thrown if the input matrix is singular. |
TooManyCallsException | is thrown if the number of calls to the function has exceeded the maximum number of iterations times the number of moving average (MA) parameters + 1. |
IncreaseErrRelException | is thrown if the bound for the relative error is too small. |
NewInitialGuessException | is thrown if the iteration has not made good progress. |
IllConditionedException | is thrown if the problem is ill-conditioned. |
TooManyIterationsException | is thrown if the maximum number of iterations is exceeded. |
TooManyFunctionEvaluationsException | is thrown if the maximum number of function evaluations is exceeded. |
TooManyJacobianEvalException | is thrown if the maximum number of Jacobian evaluations is exceeded. |
NonStationaryException | is thrown if the final maximum likelihood estimates for the time series are nonstationary. |
SingularTriangularMatrixException | is thrown if the input triangular matrix is singular. |
is an array of length z.Length or z.Length-NLost containing the optimum seasonally adjusted, autoregressive series, where NLost is the first lost observations in this series that are dropped due to differencing. If the missing values are not dropped the first NLost values of will be set to Double.NaN. The NLost property can be used to obtain the number of lost observations.
The seasonal adjustment is done by selecting optimum values for , and p in the AR model, where m is number of differences, m=sIinitial.GetLength(1) :
where is the original time series, B is the backward shift operator defined by , is Gaussian white noise with and , , , with , and is a centering parameter for the differenced series.