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

Interface ARMAFitting

All Superinterfaces:

java.io.Serializable

All Known Implementing Classes:

ConditionalSumOfSquares

public interface ARMAFitting
extends java.io.Serializable

This interface represents a fitting method for estimating φ, θ, μ and σ^2 in an ARMA model.

Some common methods are maximum likelihood, and minimizing the conditional sum of squares.

See Also:

ConditionalSumOfSquares

Method Summary

Modifier and Type	Method and Description
double	AIC() Compute the AIC of model fitting.
double	AICC() Compute the AICC of model fitting.
Matrix	covariance() Get the asymptotic covariance matrix of the estimators.
ARMAModel	getFittedARMA() Get the ARMA coefficients, φ.
Vector	stderr() Get the asymptotic standard errors of the estimators.
double	var() Get the variance of the white noise.

Method Detail

getFittedARMA

ARMAModel getFittedARMA()

Get the ARMA coefficients, φ.

Returns:

φ and θ

var

double var()

Get the variance of the white noise.

Returns:

σ^2

stderr

Vector stderr()

Get the asymptotic standard errors of the estimators.

Returns:

the asymptotic standard errors of the estimators

covariance

Matrix covariance()

Get the asymptotic covariance matrix of the estimators.

Returns:

the asymptotic covariance matrix of the estimators

AIC

double AIC()

Compute the AIC of model fitting.

Returns:

the AIC

AICC

double AICC()

Compute the AICC of model fitting.

Returns:

the AICC