Class AutoCorrelation
- java.lang.Object
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- dev.nm.analysis.function.rn2r1.AbstractRealScalarFunction
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- dev.nm.analysis.function.rn2r1.AbstractBivariateRealFunction
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- dev.nm.stat.timeseries.linear.univariate.AutoCorrelationFunction
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- dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma.AutoCorrelation
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- All Implemented Interfaces:
Function<Vector,Double>
,BivariateRealFunction
,RealScalarFunction
public class AutoCorrelation extends AutoCorrelationFunction
Compute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0. This implementation solves the Yule-Walker equation. The R equivalent functions areARMAacf
andTacvfAR
in 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 AutoCorrelation(ARMAModel model, int nLags)
Compute the auto-correlation function for an ARMA model.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description double
evaluate(double i)
Get the i-th auto-correlation.double
evaluate(double i, double j)
Evaluate y = f(x1,x2).-
Methods inherited from class dev.nm.stat.timeseries.linear.univariate.AutoCorrelationFunction
get
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Methods inherited from class dev.nm.analysis.function.rn2r1.AbstractBivariateRealFunction
evaluate
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Methods inherited from class dev.nm.analysis.function.rn2r1.AbstractRealScalarFunction
dimensionOfDomain, dimensionOfRange
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Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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Methods inherited from interface dev.nm.analysis.function.Function
dimensionOfDomain, dimensionOfRange
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Constructor Detail
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AutoCorrelation
public AutoCorrelation(ARMAModel model, int nLags)
Compute the auto-correlation function for an ARMA model.- Parameters:
model
- an ARIMA modelnLags
- the number of lags
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Method Detail
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evaluate
public double evaluate(double i, double j)
Description copied from interface:BivariateRealFunction
Evaluate y = f(x1,x2).- Parameters:
i
- x1j
- x2- Returns:
- f(x1, x2)
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evaluate
public double evaluate(double i)
Get the i-th auto-correlation.- Parameters:
i
- the lag order- Returns:
- the i-th auto-correlation
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