Uses of Class
com.imsl.stat.ARAutoUnivariate.TriangularMatrixSingularException

Packages that use ARAutoUnivariate.TriangularMatrixSingularException
Package
Description
com.imsl.stat
Statistical methods.

  • Uses of ARAutoUnivariate.TriangularMatrixSingularException in com.imsl.stat

    Methods in com.imsl.stat that throw ARAutoUnivariate.TriangularMatrixSingularException
    Modifier and Type
    Method
    Description
    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
    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, \(W_t(s,d)\).
    final 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.
    final 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.
    final 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[]
    ARAutoUnivariate.getAR()
    Returns the final auto regressive parameter estimates at the optimum AIC using the estimation method specified in setEstimationMethod .
    double[]
    ARSeasonalFit.getAR()
    Returns the final autoregressive parameter estimates at the optimum in the transformed series \(W_t\).
    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, \(W_t(s,d)\).