Class ARMAForecastOneStep
- java.lang.Object
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- dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.ARMAForecastOneStep
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public class ARMAForecastOneStep extends Object
Computes the one-step ahead prediction of a causal ARMA model, by the innovative algorithm.
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Constructor Summary
Constructors Constructor Description ARMAForecastOneStep(double[] xt, ARMAModel arma)
Makes the one-step ahead prediction for an ARMA model.ARMAForecastOneStep(IntTimeTimeSeries xt, ARMAModel arma)
Makes the one-step ahead prediction for an ARMA model.ARMAForecastOneStep(IntTimeTimeSeries xt, ARMAModel arma, InnovationsAlgorithm inn)
Makes the one-step ahead prediction for an ARMA model.
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description static AutoCovarianceFunction
K(ARMAModel arma)
double
var()
Gets the mean squared error of the one-step ahead prediction.double
var(int n)
Gets the mean squared error of the prediction at time n for \(\hat{x}_{n+1}\), i.e., \(E(x_{n+1} - \hat{x}_{n+1})^2\).double
xHat()
Gets the one-step ahead prediction of the time series.double
xHat(int n)
Gets the one-step ahead prediction \(\hat{x}_{n+1}\).
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Constructor Detail
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ARMAForecastOneStep
public ARMAForecastOneStep(IntTimeTimeSeries xt, ARMAModel arma, InnovationsAlgorithm inn)
Makes the one-step ahead prediction for an ARMA model.- Parameters:
xt
- the observationsarma
- the ARMA modelinn
- the innovation algorithm to use
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ARMAForecastOneStep
public ARMAForecastOneStep(IntTimeTimeSeries xt, ARMAModel arma)
Makes the one-step ahead prediction for an ARMA model.- Parameters:
xt
- the observationsarma
- the ARMA model
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ARMAForecastOneStep
public ARMAForecastOneStep(double[] xt, ARMAModel arma)
Makes the one-step ahead prediction for an ARMA model.- Parameters:
xt
- the observationsarma
- the ARMA model
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Method Detail
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K
public static AutoCovarianceFunction K(ARMAModel arma)
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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"
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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)
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xHat
public double xHat()
Gets the one-step ahead prediction of the time series.- Returns:
- the one-step ahead prediction
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var
public double var()
Gets the mean squared error of the one-step ahead prediction.- Returns:
- the mean squared error (variance)
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