ARMALikelihoodFactory¶

class ARMALikelihoodFactory(*args)¶

Maximum likelihood estimator of a multivariate ARMA Gaussian process.

Available constructors:

ARMALikelihoodFactory()

ARMALikelihoodFactory(p, q, d, invertible)

ARMALikelihoodFactory(indP, indQ, d, invertible)

Parameters:

pint

Order of the AR part of the $ARMA(p,q)$ process of dimension $d$ .

qint

Order of the MA part of the $ARMA(p,q)$ process of dimension $d$ .

dint, $d \geq 1$

Dimension of the process.

invertiblebool, optional

Restrict the estimation to invertible ARMA processes.

By default: True.

indPIndices

All the $p$ orders that will be investigated. Care: not yet implemented.

indQIndices

All the $p$ orders that will be investigated. Care: not yet implemented.

Methods

`build`(*args)	Estimate the ARMA process.
`getClassName`()	Accessor to the object's name.
`getCurrentP`()	Accessor to the current P order.
`getCurrentQ`()	Accessor to the current Q order.
`getInitialARCoefficients`()	Accessor to the initial AR coefficients.
`getInitialCovarianceMatrix`()	Accessor to the initial covariance matrix of the white noise.
`getInitialMACoefficients`()	Accessor to the initial MA coefficients.
`getInvertible`()	Accessor to the invertible constraint.
`getName`()	Accessor to the object's name.
`getP`()	Accessor to the P orders.
`getQ`()	Accessor to the Q orders.
`hasName`()	Test if the object is named.
`setInitialARCoefficients`(phi)	Accessor to the initial AR coefficients.
`setInitialConditions`(arCoefficients, ...)	Accessor to the initial AR coefficients.
`setInitialCovarianceMatrix`(covarianceMatrix)	Accessor to the initial covariance matrix of the white noise.
`setInitialMACoefficients`(theta)	Accessor to the initial MA coefficients.
`setInvertible`(invertible)	Accessor to the invertible constraint.
`setName`(name)	Accessor to the object's name.

Notes

We suppose here that the white noise is normal with zero mean and covariance matrix $\mat{\Sigma}_{\varepsilon} = \sigma^2\mat{Q}$ where $|\mat{Q}| = 1$ . It implies that the ARMA process estimated is normal.

Let $(t_i, \vect{X}_i)_{0\leq i \leq n-1}$ be a multivariate time series of dimension $d$ from an $ARMA(p,q)$ process.

If we note $\vect{W} = (\vect{X}_0, \hdots, \vect{X}_{n-1})$ , then $\vect{W}$ is normal with zero mean. Its covariance matrix writes $\mathbb{E}(\vect{W}\Tr{\vect{W}})= \sigma^2 \Sigma_{\vect{W}}$ which depends on the coefficients $(\mat{A}_k, \mat{B}_l)$ for $k = 1,\ldots,p$ and $l = 1,\ldots, q$ and on the matrix $\mat{Q}$ .

The likelihood of $\vect{W}$ writes :

$L(\vect{\beta}, \sigma^2 | \vect{W}) = (2 \pi \sigma^2) ^{-\frac{d n}{2}} |\Sigma_{w}|^{-\frac{1}{2}} \exp\left(- (2\sigma^2)^{-1} \Tr{\vect{W}} \Sigma_{\vect{W}}^{-1} \vect{W} \right)$

where $\vect{\beta} = (\mat{A}_{k}, \mat{B}_{l}, \mat{Q}),\ k = 1,\ldots,p$ , $l = 1,\ldots, q$ and where $|.|$ denotes the determinant.

No evaluation of selection criteria such as AIC or BIC is done.

Examples

Create a time series from a scalar ARMA(4,2) and a normal white noise:

>>> import openturns as ot
>>> myTimeGrid = ot.RegularGrid(0.0, 0.1, 50)
>>> myWhiteNoise = ot.WhiteNoise(ot.Triangular(-1.0, 0.0, 1.0), myTimeGrid)
>>> myARCoef = ot.ARMACoefficients([0.4, 0.3, 0.2, 0.1])
>>> myMACoef = ot.ARMACoefficients([0.4, 0.3])
>>> myARMAProcess = ot.ARMA(myARCoef, myMACoef, myWhiteNoise)
>>> myTimeSeries = myARMAProcess.getRealization()

Estimate the ARMA process with the maximum likelihood estimator:

>>> myFactory = ot.ARMALikelihoodFactory(4, 2, 1)
>>> myARMA = myFactory.build(ot.TimeSeries(myTimeSeries))

__init__(*args)¶

build(*args)¶

Estimate the ARMA process.

Available usages:

build(myTimeSeries)

build(myProcessSample)

Parameters:

myTimeSeriesTimeSeries: One realization of the process.
myProcessSampleProcessSample: Several realizations of the process.

Returns:

myARMAARMA: The process estimated with the maximum likelihood estimator.

getClassName()¶

Accessor to the object’s name.

Returns:

class_namestr: The object class name (object.__class__.__name__).

getCurrentP()¶

Accessor to the current P order.

Returns:

pint: Order of the AR part of the $ARMA(p,q)$ process of dimension $d$ .

getCurrentQ()¶

Accessor to the current Q order.

Returns:

qint: Order of the MA part of the $ARMA(p,q)$ process of dimension $d$ .

getInitialARCoefficients()¶

Accessor to the initial AR coefficients.

Returns:

initARCoeffARMACoefficients: The initial AR coefficients used for the optimization algorithm.

getInitialCovarianceMatrix()¶

Accessor to the initial covariance matrix of the white noise.

Returns:

initCovMatCovarianceMatrix: The initial covariance matrix of the white noise used for the optimization algorithm.

getInitialMACoefficients()¶

Accessor to the initial MA coefficients.

Returns:

initMACoeffARMACoefficients: The initial MA coefficients used for the optimization algorithm.

getInvertible()¶

Accessor to the invertible constraint.

Returns:

invertiblebool: The initial AR coefficients used for the optimization algorithm.

getName()¶

Accessor to the object’s name.

Returns:

namestr: The name of the object.

getP()¶

Accessor to the P orders.

Returns:

indPIndices: All the $p$ orders that will be investigated.

getQ()¶

Accessor to the Q orders.

Returns:

indQIndices: All the $p$ orders that will be investigated.

hasName()¶

Test if the object is named.

Returns:

hasNamebool: True if the name is not empty.

setInitialARCoefficients(phi)¶

Accessor to the initial AR coefficients.

Parameters:

initARCoeffARMACoefficients: The initial AR coefficients used for the optimization algorithm.

setInitialConditions(arCoefficients, maCoefficients, covarianceMatrix)¶

Accessor to the initial AR coefficients.

Parameters:

initARCoeffARMACoefficients: The initial AR coefficients used for the optimization algorithm.
initMACoeffARMACoefficients: The initial AR coefficients used for the optimization algorithm.
initCovMatrCovarianceMatrix: The initial covariance matrix of the white noise used for the optimization algorithm.

setInitialCovarianceMatrix(covarianceMatrix)¶

Accessor to the initial covariance matrix of the white noise.

Parameters:

initCovMatCovarianceMatrix: The initial covariance matrix of the white noise used for the optimization algorithm.

setInitialMACoefficients(theta)¶

Accessor to the initial MA coefficients.

Parameters:

initMACoeffARMACoefficients: The initial MA coefficients used for the optimization algorithm.

setInvertible(invertible)¶

Accessor to the invertible constraint.

Parameters:

invertiblebool: The initial AR coefficients used for the optimization algorithm.

setName(name)¶

Accessor to the object’s name.

Parameters:

namestr: The name of the object.

Examples using the class¶

Estimate a multivariate ARMA process

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ARMALikelihoodFactory¶

Examples using the class¶