Module ojalgo

Package org.ojalgo.random.process

Class StationaryNormalProcess

java.lang.Object

org.ojalgo.random.process.StationaryNormalProcess

All Implemented Interfaces:

RandomProcess<Normal>

public final class StationaryNormalProcess
extends Object

Process with fixed mean and (possibly) fluctuating variance given by a ScedasticityModel.

Author:

apete

Nested Class Summary

Nested classes/interfaces inherited from interface org.ojalgo.random.process.RandomProcess

RandomProcess.SimulationResults

Method Summary

Modifier and Type	Method	Description
static StationaryNormalProcess	estimateARCH(Access1D<?> series, int q)
static StationaryNormalProcess	estimateGARCH(Access1D<?> series, int p, int q)
Normal	getDistribution(double evaluationPoint)
final double	getExpected()	Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getExpected().
final double	getLowerConfidenceQuantile(double confidence)	The same thing can be achieved by first calling RandomProcess.getDistribution(double) with argumant 1.0, and then ContinuousDistribution.getQuantile(double) (but with different input argument).
final double	getStandardDeviation()	Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getStandardDeviation().
final double	getUpperConfidenceQuantile(double confidence)	The same thing can be achieved by first calling RandomProcess.getDistribution(double) with argumant 1.0, and then ContinuousDistribution.getQuantile(double) (but with different input argument).
double	getValue()
final double	getVariance()	Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getVariance().
static StationaryNormalProcess	of(ScedasticityModel scedasticityModel)
void	setValue(double newValue)
RandomProcess.SimulationResults	simulate(int numberOfRealisations, int numberOfSteps, double stepSize)	Returns an collection of sample sets.
double	step()
double	step(double stepSize, double standardGaussianInnovation)

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.ojalgo.random.process.RandomProcess

simulate

Method Details

estimateARCH

public static StationaryNormalProcess estimateARCH(Access1D<?> series,
 int q)

estimateGARCH

public static StationaryNormalProcess estimateGARCH(Access1D<?> series,
 int p,
 int q)

of

public static StationaryNormalProcess of(ScedasticityModel scedasticityModel)

getDistribution

public Normal getDistribution(double evaluationPoint)

Specified by:

getDistribution in interface RandomProcess<Normal>

Parameters:

evaluationPoint - How far into the future?

Returns:

The distribution for the process value at that future time.

getValue

public double getValue()

setValue

public void setValue(double newValue)

step

public double step()

step

public double step(double stepSize,
 double standardGaussianInnovation)

simulate

public RandomProcess.SimulationResults simulate(int numberOfRealisations,
 int numberOfSteps,
 double stepSize)

Description copied from interface: RandomProcess

Returns an collection of sample sets. The array has numberOfSteps elements, and each sample set has numberOfRealisations samples.

Returns:

An array of sample sets. The array has aNumberOfSteps elements, and each sample set has aNumberOfRealisations samples.

getExpected

public final double getExpected()

Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getExpected().

getLowerConfidenceQuantile

public final double getLowerConfidenceQuantile(double confidence)

The same thing can be achieved by first calling RandomProcess.getDistribution(double) with argumant 1.0, and then ContinuousDistribution.getQuantile(double) (but with different input argument).

getStandardDeviation

public final double getStandardDeviation()

Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getStandardDeviation().

getUpperConfidenceQuantile

public final double getUpperConfidenceQuantile(double confidence)

The same thing can be achieved by first calling RandomProcess.getDistribution(double) with argumant 1.0, and then ContinuousDistribution.getQuantile(double) (but with different input argument).

getVariance

public final double getVariance()

Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getVariance().