Package dev.nm.stat.timeseries.linear.univariate.sample

Class SampleAutoCorrelation

java.lang.Object

dev.nm.analysis.function.rn2r1.AbstractRealScalarFunction

dev.nm.analysis.function.rn2r1.AbstractBivariateRealFunction

dev.nm.stat.timeseries.linear.univariate.AutoCorrelationFunction

dev.nm.stat.timeseries.linear.univariate.sample.SampleAutoCorrelation

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.

Nested Class Summary

Nested classes/interfaces inherited from interface dev.nm.analysis.function.Function

Function.EvaluationException

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.

Method Summary

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 lag k.

Methods inherited from class dev.nm.stat.timeseries.linear.univariate.AutoCorrelationFunction

get

Methods inherited from class dev.nm.analysis.function.rn2r1.AbstractBivariateRealFunction

evaluate

Methods inherited from class dev.nm.analysis.function.rn2r1.AbstractRealScalarFunction

dimensionOfDomain, dimensionOfRange

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface dev.nm.analysis.function.Function

dimensionOfDomain, dimensionOfRange

Constructor Details

SampleAutoCorrelation

public SampleAutoCorrelation(IntTimeTimeSeries xt,
 SampleAutoCovariance.Type type)

Construct the sample ACF for a time series.

Parameters:

xt - a time series

type - the auto-covariance type

SampleAutoCorrelation

public SampleAutoCorrelation(IntTimeTimeSeries xt)

Construct the sample ACF for a time series.

Parameters:

xt - a time series

Method Details

evaluate

public double evaluate(int k)

Compute the auto-correlation for lag k.

Parameters:

k - the lag order

Returns:

ρ(k)

evaluate

public double evaluate(double i,
 double j)

Description copied from interface: BivariateRealFunction

Evaluate y = f(x₁,x₂).

Parameters:

i - x₁

j - x₂

Returns:

f(x₁, x₂)