Uses of Class
com.imsl.stat.ARMA.ResidualsTooLargeException

Packages that use ARMA.ResidualsTooLargeException

Package	Description
com.imsl.stat	Statistical methods.

Uses of ARMA.ResidualsTooLargeException in com.imsl.stat

Methods in com.imsl.stat that throw ARMA.ResidualsTooLargeException

Modifier and Type	Method and Description
void	ARSeasonalFit.compute() Computes the minimum AIC and optimum values for s and d based upon the candidates provided in sInitial and dInitial, and computes the values for the transformed series, \(W_t(s,d)\).
void	ARAutoUnivariate.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 setEstimationMethod.
void	ARMA.compute() Computes least-square estimates of parameters for an ARMA model.
void	AutoARIMA.compute(int maxlag) Estimates potential missing values, detects and determines outliers and simultaneously fits an optimum model from a set of different \( \text{ARIMA}(p,0,0)\times(0,d,0)_s\) models to the outlier free time series.
void	ARMAOutlierIdentification.compute(int[] model) Detects and determines outliers and simultaneously estimates the model parameters for the given time series.
void	AutoARIMA.compute(int[] arOrders, int[] maOrders) Estimates potential missing values, detects and determines outliers and simultaneously fits an optimum model from a set of different \( \text{ARIMA}(p,0,q)\times(0,d,0)_s\) models to the outlier free time series.
void	AutoARIMA.compute(int p, int q, int s, int d) Estimates potential missing values, detects and determines outliers and simultaneously fits an \(\text{ARIMA}(p,0,q)\times(0,d,0)_s \) model to the outlier free time series.
double[][]	ARAutoUnivariate.forecast(int nForecast) Returns forecasts and associated confidence interval offsets.
double	ARAutoUnivariate.getAIC() Returns the final estimate for Akaike's Information Criterion (AIC) at the optimum.
double[]	ARSeasonalFit.getAR() Returns the final autoregressive parameter estimates at the optimum in the transformed series \(W_t\).
double[]	ARAutoUnivariate.getAR() Returns the final auto regressive parameter estimates at the optimum AIC using the estimation method specified in setEstimationMethod .
double[]	ARMAEstimateMissing.getCompleteTimeSeries() Returns a double precision vector of length tpoints[tpoints.length-1]-tpoints[0]+1 containing the observed values in the time series z plus estimates for missing values in gaps identified in tpoints.
double	ARAutoUnivariate.getConstant() Returns the estimate for the constant parameter in the ARMA series.
double[]	ARAutoUnivariate.getForecast(int nForecast) Returns forecasts
double	ARAutoUnivariate.getInnovationVariance() Returns the final estimate for the innovation variance.
double	ARAutoUnivariate.getLikelihood() Returns the final estimate for \(L=e^{-(\mbox{AIC} - 2p)/2} \), where p is the AR order, AIC is Akaike's Information Criterion, and L is the likelihood function evaluated for the optimum autoregressive model.
double	ARAutoUnivariate.getMean() Returns the mean used to center the time series z.
int	ARSeasonalFit.getNLost() Returns the number of values in the initial part of the series lost to differencing.
int[]	ARSeasonalFit.getOptimumD() Returns the optimum values for d selected among the candidates in dInitial.
int[]	ARSeasonalFit.getOptimumS() Returns the optimum values for s selected among the candidates in sInitial.
int	ARAutoUnivariate.getOrder() Returns the order of the AR model selected with the minimum AIC.
double[]	ARAutoUnivariate.getTimsacAR() Returns the final auto regressive parameter estimates at the optimum AIC estimated by the original TIMSAC routine (UNIMAR).
double	ARAutoUnivariate.getTimsacConstant() Returns the estimate for the constant parameter in the ARMA series.
double	ARAutoUnivariate.getTimsacVariance() Returns the final estimate for the innovation variance calculated by the TIMSAC automatic AR modeling routine (UNIMAR).
double[]	ARSeasonalFit.getTransformedTimeSeries() Returns the transformed series, \(W_t(s,d)\).