WhittleFactory¶

class
WhittleFactory
(*args)¶ Whittle estimator of a scalar ARMA normal process.
 Available constructors:
WhittleFactory()
WhittleFactory(p, q, invert)
WhittleFactory(indP, indQ, invertible)
Parameters: p : int
Order of the AR part of the process of dimension .
q : int
Order of the MA part of the process of dimension .
invertible : bool, optional
Restrict the estimation to invertible ARMA processes.
By default: True.
indP :
Indices
All the orders that will be investigated. Care: not yet implemented.
indQ :
Indices
All the orders that will be investigated. Care: not yet implemented.
Notes
We suppose here that the white noise is normal with zero mean and variance . It implies that the ARMA process estimated is normal.
For each order , the estimation of the coefficients , and the variance is done using the Whittle estimator which is based on the maximization of the likelihood function in the frequency domain.
The principle is detailed hereafter for the case of a time series : in the case of a process sample, the estimator is similar except for the periodogram which is computed differently.
Let be a multivariate time series of dimension from an process.
The spectral density function of the process writes :
where and is the frequency value.
The Whittle loglikelihood writes :
where :
 is the non parametric estimate of the spectral density, expressed in the Fourier space (frequencies in instead of ). OpenTURNS uses by default the Welch estimator.
 is the Fourier frequency, , with the largest integer .
We estimate the scalar coefficients by maximizing the loglikelihood function. The corresponding equations lead to the following relation :
where maximizes :
The Whitle estimation requires that :
 the determinant of the eigen values of the companion matrix associated to the polynomial are outside the unit disc, which garantees the stationarity of the process;
 the determinant of the eigen values of thez companion matrix associated to the polynomial are outside the unit disc, which garantees the invertibility of the process.
The criteria AIC, (corrected AIC) and BIC are evaluated to help the model selection:
where is half the number of points of the time grid of the process sample (if the data are a process sample) or in a block of the time series (if the data are a time series).
The BIC criterion leads to a model that gives a better prediction. The AIC criterion selects the best model that fits the given data. The criterion improves the previous one by penalizing a too high order that would artificially fit to the data.
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, 100) >>> 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(10)
Estimate the ARMA process specifying the orders:
>>> myFactory_42 = ot.WhittleFactory(4, 2)
Check the default SpectralModelFactory:
>>> #print(myFactory_42.getSpectralModelFactory())
Set a particular spectral model: WelchFactory as SpectralModelFactory with the Hanning filtering window:
>>> myFilteringWindow = ot. Hanning() >>> mySpectralFactory = ot.WelchFactory(myFilteringWindow, 4, 0) >>> myFactory_42.setSpectralModelFactory(mySpectralFactory) >>> #print(myFactory_42.getSpectralModelFactory())
Estimate the ARMA process specifying a range for the orders:
p = [1, 2, 4] and q = [4,5,6]:
>>> pIndices = ot.Indices([1, 2, 4]) >>> qIndices = ot.Indices(3) >>> qIndices.fill(4,1) >>> myFactory_Range = ot.WhittleFactory(pIndices, qIndices)
To get the quantified AICc, AIC and BIC criteria:
>>> myARMA_42, myCriterion = myFactory_42.buildWithCriteria(ot.TimeSeries(myTimeSeries)) >>> AICc, AIC, BIC = myCriterion[0:3]
Methods
build
(*args)Estimate the ARMA process. buildWithCriteria
(*args)Estimate the ARMA process. clearHistory
()Clear the history of the factory. disableHistory
()Desactivate the history of all the estimated models. enableHistory
()Activate the history of all the estimated models. getClassName
()Accessor to the object’s name. getCurrentP
()getCurrentQ
()getHistory
()Check whether the history mecanism is activated. getId
()Accessor to the object’s id. getInvertible
()getName
()Accessor to the object’s name. getP
()getQ
()getShadowedId
()Accessor to the object’s shadowed id. getSpectralModelFactory
()Accessor to the spectral factory. getStartingPoints
()Accessor to the starting points for the optimization step. getVerbose
()Accessor to the verbose mode. getVisibility
()Accessor to the object’s visibility state. hasName
()Test if the object is named. hasVisibleName
()Test if the object has a distinguishable name. isHistoryEnabled
()Check whether the history mecanism is activated. setInvertible
(invertible)setName
(name)Accessor to the object’s name. setShadowedId
(id)Accessor to the object’s shadowed id. setSpectralModelFactory
(factory)Accessor to the spectral factory. setStartingPoints
(startingPoints)Accessor to the starting points for the optimization step. setVerbose
(verbose)Accessor to the verbose mode. setVisibility
(visible)Accessor to the object’s visibility state. 
__init__
(*args)¶

build
(*args)¶ Estimate the ARMA process.
 Available usages:
build(myTimeSeries)
build(myProcessSample)
Parameters: myTimeSeries :
TimeSeries
One realization of the process.
myProcessSample :
ProcessSample
Several realizations of the process.
Returns: myARMA :
ARMA
The process estimated with the Whittle estimator.
Notes
The model selection is made using the spectral density built using the given data and theoretical spectral density of the ARMA process.
The best ARMA process is selected according to the corrected AIC criterion.

buildWithCriteria
(*args)¶ Estimate the ARMA process.
 Available usages:
buildWithCriteria(myTimeSeries)
buildWithCriteria(myProcessSample)
Parameters: myTimeSeries :
TimeSeries
One realization of the process.
myProcessSample :
ProcessSample
Several realizations of the process.
Returns: myARMA :
ARMA
The process estimated with the Whittle estimator.
criterion :
Point
Result of the evaluation of the AICc, AIC and BIC criteria
Notes
The model selection is made using the spectral density built using the given data and theoretical spectral density of the ARMA process.
The best ARMA process is selected according to the corrected AIC criterion.

clearHistory
()¶ Clear the history of the factory.
Notes
Clear the history of the factory.

disableHistory
()¶ Desactivate the history of all the estimated models.
Notes
Desactivate the history mechanism which is the trace of all the tested models and their associated information criteria.

enableHistory
()¶ Activate the history of all the estimated models.
Notes
Activate the history mechanism which is the trace of all the tested models and their associated information criteria.
By default, the history mecanism is activated.

getClassName
()¶ Accessor to the object’s name.
Returns: class_name : str
The object class name (object.__class__.__name__).

getHistory
()¶ Check whether the history mecanism is activated.
Returns: histMec : a list of
WhittleFactoryState
Returns the collection of all the states that have been built for the estimation.

getId
()¶ Accessor to the object’s id.
Returns: id : int
Internal unique identifier.

getName
()¶ Accessor to the object’s name.
Returns: name : str
The name of the object.

getShadowedId
()¶ Accessor to the object’s shadowed id.
Returns: id : int
Internal unique identifier.

getSpectralModelFactory
()¶ Accessor to the spectral factory.
Returns: initARCoeff :
SpectralModelFactory
The spectral factory used to estimate the spectral density based on the data.

getStartingPoints
()¶ Accessor to the starting points for the optimization step.
Returns: startPointsList : a list of
Point
Starting points for the optimization step, for each pair of orders that will be tested.

getVerbose
()¶ Accessor to the verbose mode.
Returns: verboseMode : bool
Get the verbose mode while both the exploration of the possible models and the optimization steps.

getVisibility
()¶ Accessor to the object’s visibility state.
Returns: visible : bool
Visibility flag.

hasName
()¶ Test if the object is named.
Returns: hasName : bool
True if the name is not empty.

hasVisibleName
()¶ Test if the object has a distinguishable name.
Returns: hasVisibleName : bool
True if the name is not empty and not the default one.

isHistoryEnabled
()¶ Check whether the history mecanism is activated.
Returns: histMec : bool
Check whether the history mecanism is activated.
By default, the history mecanism is activated.

setName
(name)¶ Accessor to the object’s name.
Parameters: name : str
The name of the object.

setShadowedId
(id)¶ Accessor to the object’s shadowed id.
Parameters: id : int
Internal unique identifier.

setSpectralModelFactory
(factory)¶ Accessor to the spectral factory.
Parameters: spectralModelFact :
SpectralModelFactory
The spectral factory used to estimate the spectral density based on the data.

setStartingPoints
(startingPoints)¶ Accessor to the starting points for the optimization step.
Parameters: startPointsList : a list of
Point
Starting points for the optimization step, for each pair of orders that will be tested.

setVerbose
(verbose)¶ Accessor to the verbose mode.
Parameters: verboseMode : bool
Set the verbose mode while both the exploration of the possible models and the optimization steps.

setVisibility
(visible)¶ Accessor to the object’s visibility state.
Parameters: visible : bool
Visibility flag.