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

Class AutoCorrelationFunction

java.lang.Object

com.numericalmethod.suanshu.analysis.function.rn2r1.AbstractRealScalarFunction

com.numericalmethod.suanshu.analysis.function.rn2r1.AbstractBivariateRealFunction

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

All Implemented Interfaces:

Function<Vector,Double>, BivariateRealFunction, RealScalarFunction

Direct Known Subclasses:

AutoCorrelation, SampleAutoCorrelation, SamplePartialAutoCorrelation

public abstract class AutoCorrelationFunction
extends AbstractBivariateRealFunction

This is the auto-correlation function of a univariate time series {x_t}. For a stationary process, the auto-correlation depends only on the lag, |i - j|.

Nested Class Summary

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

Function.EvaluationException

Constructor Summary

Constructors

Constructor and Description
AutoCorrelationFunction()

Method Summary

Modifier and Type	Method and Description
double	get(int i, int j) Get the auto-correlation of xi and xj.

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

evaluate

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

evaluate

Methods inherited from interface com.numericalmethod.suanshu.analysis.function.Function

dimensionOfDomain, dimensionOfRange

Constructor Detail

AutoCorrelationFunction

public AutoCorrelationFunction()

Method Detail

get

public double get(int i,
                  int j)

Get the auto-correlation of x_i and x_j.

Parameters:

i - i > 0

j - j > 0

Returns:

the correlation of x_i and x_j