Class SampleAutoCorrelation
- 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.sample.SampleAutoCorrelation
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- All Implemented Interfaces:
Function<Vector,Double>
,BivariateRealFunction
,RealScalarFunction
public class SampleAutoCorrelation extends AutoCorrelationFunction
This is the sample Auto-Correlation Function (ACF) for a univariate data set.
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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 SampleAutoCorrelation(IntTimeTimeSeries xt)
Construct the sample ACF for a time series.SampleAutoCorrelation(IntTimeTimeSeries xt, SampleAutoCovariance.Type type)
Construct the sample ACF for a time series.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description double
evaluate(double i, double j)
Evaluate y = f(x1,x2).double
evaluate(int k)
Compute the auto-correlation for lagk
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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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SampleAutoCorrelation
public SampleAutoCorrelation(IntTimeTimeSeries xt, SampleAutoCovariance.Type type)
Construct the sample ACF for a time series.- Parameters:
xt
- a time seriestype
- the auto-covariance type
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SampleAutoCorrelation
public SampleAutoCorrelation(IntTimeTimeSeries xt)
Construct the sample ACF for a time series.- Parameters:
xt
- a time series
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Method Detail
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evaluate
public double evaluate(int k)
Compute the auto-correlation for lagk
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k
- the lag order- Returns:
- ρ(k)
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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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