WhittleFactoryState

class WhittleFactoryState(*args)

Last state recorded of a scalar ARMA process.

Parameters:

pint

AR order of the estimated .

ARMACoeffsequence of float of dimension

Coefficients of the AR then MA part of the estimated process.

sigma2float

Variance of the white noise.

criteriasequence of float of dimension 3

The values of the criteria AIC, (corrected AIC) and BIC on the estimated process.

timeGridRegularGrid

Time grid of the process.

Methods

getARCoefficients()	Accessor to the AR coefficients of the scalar ARMA process.
getARMA()	Accessor to the estimated scalar ARMA.
getClassName()	Accessor to the object's name.
getInformationCriteria()	Accessor to the values of the criteria AIC, and BIC.
getMACoefficients()	Accessor to the MA coefficients of the scalar ARMA process.
getName()	Accessor to the object's name.
getP()	Accessor to AR order.
getQ()	Accessor to MA order.
getSigma2()	Accessor to the variance of the white noise.
getTheta()	Accessor to the coefficients of the scalar ARMA process.
getTimeGrid()	Accessor to the time grid of the scalar ARMA process.
getWhiteNoise()	Accessor to the white noise defining the scalar ARMA process.
hasName()	Test if the object is named.
setName(name)	Accessor to the object's name.

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, 20)
>>> 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()
>>> myProcessSample = myARMAProcess.getSample(20)

Estimate the ARMA process specifying a range for the orders:

>>> pIndices = [1, 2]
>>> qIndices =  [4, 5]
>>> myFactory_Range = ot.WhittleFactory(pIndices, qIndices)

Print all the models and their state:

>>> myWhittleHistory = myFactory_Range.getHistory()
>>> for i in range(myWhittleHistory.getSize()):
...     model_i = myWhittleHistory[i]
...     arma = model_i.getARMA()
...     print('Order(p,q) = '+str(model_i.getP())+', '+str(model_i.getQ()))
...     print('AR coeff = '+str(model_i.getARCoefficients()))
...     print('MA coeff = '+str(model_i.getMACoefficients()))
...     print('White Noise - Sigma = '+str(model_i.getSigma2()))
...     print('Criteria AICc, AIC, BIC = '+str(model_i.getInformationCriteria()))

__init__(*args)

getARCoefficients()

Accessor to the AR coefficients of the scalar ARMA process.

Returns:

ARCoeffARMACoefficients

The AR coefficients of the linear recurrence defining the process.

getARMA()

Accessor to the estimated scalar ARMA.

Returns:

MACoeffARMA

The estimated ARMA model.

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

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

getInformationCriteria()

Accessor to the values of the criteria AIC, and BIC.

Returns:

criteriaPoint of dimension 3

Values of the criteria AIC, (corrected AIC) and BIC of the estimated model.

getMACoefficients()

Accessor to the MA coefficients of the scalar ARMA process.

Returns:

MACoeffARMACoefficients

The MA coefficients of the linear recurrence defining the process.

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

getP()

Accessor to AR order.

Returns:

pint

Order of the AR part of the estimated model.

getQ()

Accessor to MA order.

Returns:

qint

Order of the MA part of the estimated model.

getSigma2()

Accessor to the variance of the white noise.

Parameters:

sigma2positive float

Variance of the white noise.

getTheta()

Accessor to the coefficients of the scalar ARMA process.

Returns:

coeffPoint

The AR and MA coefficients of the linear recurrence defining the process.

getTimeGrid()

Accessor to the time grid of the scalar ARMA process.

Returns:

timeGridRegularGrid

Time grid over which the ARMA process is defined.

getWhiteNoise()

Accessor to the white noise defining the scalar ARMA process.

Returns:

whiteNoiseWhiteNoise

The white noise of the estimated model.

hasName()

Test if the object is named.

Returns:

hasNamebool

True if the name is not empty.

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.