com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample

Class AutoCovariance

java.lang.Object

com.numericalmethod.suanshu.analysis.function.rn2r1.BivariateRealFunction

com.numericalmethod.suanshu.stats.timeseries.linear.univariate.AutoCovarianceFunction

com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample.AutoCovariance

All Implemented Interfaces:

Function<Vector,java.lang.Double>, RealScalarFunction, java.io.Serializable

public class AutoCovariance
extends AutoCovarianceFunction

This computes the sample Auto-Covariance Function (ACVF) for a univariate data set.

See Also:

"William W.S. Wei, "Section 2.5.2. Sample Auto-covariance function" in Time Series Analysis : Univariate and Multivariate Methods (2nd Edition), Addison Wesley; 2 edition (July 17, 2005)", Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	AutoCovariance.Type

Nested classes/interfaces inherited from interface com.numericalmethod.suanshu.analysis.function.Function

Function.EvaluationException

Constructor Summary

Constructors

Constructor and Description
AutoCovariance(TimeSeries xt)
AutoCovariance(TimeSeries xt, AutoCovariance.Type type)

Method Summary

Modifier and Type	Method and Description
double	evaluate(double x1, double x2) Evaluate y = f(x1,x2).
double	evaluate(int k) Compute the auto-covariance for lag k.

Methods inherited from class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.AutoCovarianceFunction

get

Methods inherited from class com.numericalmethod.suanshu.analysis.function.rn2r1.BivariateRealFunction

dimensionOfDomain, dimensionOfRange, evaluate

Methods inherited from class java.lang.Object

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

Constructor Detail

AutoCovariance

public AutoCovariance(TimeSeries xt,
                      AutoCovariance.Type type)

AutoCovariance

public AutoCovariance(TimeSeries xt)

Method Detail

evaluate

public double evaluate(int k)

Compute the auto-covariance for lag k.

Parameters:

k - lag

Returns:

γ(k)

evaluate

public double evaluate(double x1,
                       double x2)

Description copied from class: BivariateRealFunction

Evaluate y = f(x₁,x₂).

Specified by:

evaluate in class BivariateRealFunction

Parameters:

x1 - x₁

x2 - x₂

Returns:

f(x₁, x₂)