Class ARMAForecastOneStep


  • public class ARMAForecastOneStep
    extends Object
    Computes the one-step ahead prediction of a causal ARMA model, by the innovative algorithm.
    • Constructor Detail

      • ARMAForecastOneStep

        public ARMAForecastOneStep​(IntTimeTimeSeries xt,
                                   ARMAModel arma,
                                   InnovationsAlgorithm inn)
        Makes the one-step ahead prediction for an ARMA model.
        Parameters:
        xt - the observations
        arma - the ARMA model
        inn - the innovation algorithm to use
      • ARMAForecastOneStep

        public ARMAForecastOneStep​(IntTimeTimeSeries xt,
                                   ARMAModel arma)
        Makes the one-step ahead prediction for an ARMA model.
        Parameters:
        xt - the observations
        arma - the ARMA model
      • ARMAForecastOneStep

        public ARMAForecastOneStep​(double[] xt,
                                   ARMAModel arma)
        Makes the one-step ahead prediction for an ARMA model.
        Parameters:
        xt - the observations
        arma - the ARMA model
    • Method Detail

      • xHat

        public double xHat​(int n)
        Gets the one-step ahead prediction \(\hat{x}_{n+1}\).
        Parameters:
        n - time, ranging from 0 to T, the end of observation time
        Returns:
        the one-step prediction \(\hat{x}_{n+1}\)
        See Also:
        "eq. 5.3.9"
      • var

        public double var​(int n)
        Gets the mean squared error of the prediction at time n for \(\hat{x}_{n+1}\), i.e., \(E(x_{n+1} - \hat{x}_{n+1})^2\).
        Parameters:
        n - time, ranging from 0 to T, the end of observation time
        Returns:
        the mean squared error (variance)
      • xHat

        public double xHat()
        Gets the one-step ahead prediction of the time series.
        Returns:
        the one-step ahead prediction
      • var

        public double var()
        Gets the mean squared error of the one-step ahead prediction.
        Returns:
        the mean squared error (variance)