Class VARMAAutoCorrelation
- java.lang.Object
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- dev.nm.analysis.function.matrix.R2toMatrix
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- dev.nm.stat.timeseries.linear.multivariate.MultivariateAutoCorrelationFunction
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- dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAAutoCorrelation
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- All Implemented Interfaces:
Function<Vector,Matrix>,RntoMatrix
public class VARMAAutoCorrelation extends MultivariateAutoCorrelationFunction
Compute the Auto-Correlation Function (ACF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0. This implementation solves the Yule-Walker equation. The R equivalent functions areARMAacfandTacvfARin packageFitAR.
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Nested Class Summary
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Nested classes/interfaces inherited from interface dev.nm.analysis.function.Function
Function.EvaluationException
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Constructor Summary
Constructors Constructor Description VARMAAutoCorrelation(VARMAModel model, int nLags)Compute the auto-correlation function for a vector ARMA model.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description Matrixevaluate(double i)Get the i-th auto-correlation matrix.Matrixevaluate(double i, double j)Evaluate f(x1, x2) = A.-
Methods inherited from class dev.nm.stat.timeseries.linear.multivariate.MultivariateAutoCorrelationFunction
get
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Methods inherited from class dev.nm.analysis.function.matrix.R2toMatrix
dimensionOfDomain, dimensionOfRange, evaluate
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Constructor Detail
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VARMAAutoCorrelation
public VARMAAutoCorrelation(VARMAModel model, int nLags)
Compute the auto-correlation function for a vector ARMA model.- Parameters:
model- an ARIMA modelnLags- the number of lags
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Method Detail
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evaluate
public Matrix evaluate(double i, double j)
Description copied from class:R2toMatrixEvaluate f(x1, x2) = A.- Specified by:
evaluatein classR2toMatrix- Parameters:
i-x1j-x2- Returns:
f(x1, x2)
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evaluate
public Matrix evaluate(double i)
Get the i-th auto-correlation matrix.- Parameters:
i- the lag order- Returns:
- the i-th auto-correlation matrix
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