Process¶
(Source code, png, hires.png, pdf)

class
Process
(*args)¶ Base class for stochastic processes.
Notes
The Process class enables to model a stochastic process.
A multivariate stochastic process of dimension is defined by:
where is an event, is a domain of discretized on the mesh , is a multivariate index and .
A realization of the process , for a given is defined by:
is the random variable at index defined by:
A Process object can be created only through its derived classes:
SpectralGaussianProcess
,GaussianProcess
,CompositeProcess
,ARMA
,RandomWalk
,FunctionalBasisProcess
andWhiteNoise
.Methods
Accessor to the object’s name.
Get a continuous realization.
Accessor to the covariance model.
Get the description of the process.
getFuture
(*args)Prediction of the future iterations of the process.
getId
()Accessor to the object’s id.
Accessor to the underlying implementation.
Get the dimension of the domain .
getMarginal
(*args)Get the marginal of the random process.
getMesh
()Get the mesh.
getName
()Accessor to the object’s name.
Get the dimension of the domain .
Get a realization of the process.
getSample
(size)Get realizations of the process.
Get the time grid of observation of the process.
getTrend
()Accessor to the trend.
Test whether the process is composite or not.
isNormal
()Test whether the process is normal or not.
Test whether the process is stationary or not.
setDescription
(description)Set the description of the process.
setMesh
(mesh)Set the mesh.
setName
(name)Accessor to the object’s name.
setTimeGrid
(timeGrid)Set the time grid of observation of the process.

__init__
(*args)¶ Initialize self. See help(type(self)) for accurate signature.

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

getContinuousRealization
()¶ Get a continuous realization.
 Returns
 realization
Function
According to the process, the continuous realizations are built:
either using a dedicated functional model if it exists: e.g. a functional basis process.
or using an interpolation from a discrete realization of the process on : in dimension , a linear interpolation and in dimension , a piecewise constant function (the value at a given position is equal to the value at the nearest vertex of the mesh of the process).
 realization

getCovarianceModel
()¶ Accessor to the covariance model.
 Returns
 cov_model
CovarianceModel
Covariance model, if any.
 cov_model

getDescription
()¶ Get the description of the process.
 Returns
 description
Description
Description of the process.
 description

getFuture
(*args)¶ Prediction of the future iterations of the process.
 Parameters
 stepNumberint,
Number of future steps.
 sizeint, , optional
Number of futures needed. Default is 1.
 Returns
 prediction
ProcessSample
orTimeSeries
future iterations of the process. If , prediction is a
TimeSeries
. Otherwise, it is aProcessSample
.
 prediction

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

getImplementation
()¶ Accessor to the underlying implementation.
 Returns
 implImplementation
The implementation class.

getInputDimension
()¶ Get the dimension of the domain .
 Returns
 nint
Dimension of the domain : .

getMarginal
(*args)¶ Get the marginal of the random process.
 Parameters
 kint or list of ints
Index of the marginal(s) needed.
 Returns
 marginals
Process
Process defined with marginal(s) of the random process.
 marginals

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

getOutputDimension
()¶ Get the dimension of the domain .
 Returns
 dint
Dimension of the domain .

getRealization
()¶ Get a realization of the process.
 Returns
 realization
Field
Contains a mesh over which the process is discretized and the values of the process at the vertices of the mesh.
 realization

getSample
(size)¶ Get realizations of the process.
 Parameters
 nint,
Number of realizations of the process needed.
 Returns
 processSample
ProcessSample
realizations of the random process. A process sample is a collection of fields which share the same mesh .
 processSample

getTimeGrid
()¶ Get the time grid of observation of the process.
 Returns
 timeGrid
RegularGrid
Time grid of a process when the mesh associated to the process can be interpreted as a
RegularGrid
. We check if the vertices of the mesh are scalar and are regularly spaced in but we don’t check if the connectivity of the mesh is conform to the one of a regular grid (without any hole and composed of ordered instants).
 timeGrid

getTrend
()¶ Accessor to the trend.
 Returns
 trend
TrendTransform
Trend, if any.
 trend

isComposite
()¶ Test whether the process is composite or not.
 Returns
 isCompositebool
True if the process is composite (built upon a function and a process).

isNormal
()¶ Test whether the process is normal or not.
 Returns
 isNormalbool
True if the process is normal.
Notes
A stochastic process is normal if all its finite dimensional joint distributions are normal, which means that for all and , with , there is and such that:
where , and and is the symmetric matrix:
A Gaussian process is entirely defined by its mean function and its covariance function (or correlation function ).

isStationary
()¶ Test whether the process is stationary or not.
 Returns
 isStationarybool
True if the process is stationary.
Notes
A process is stationary if its distribution is invariant by translation: , , , we have:

setDescription
(description)¶ Set the description of the process.
 Parameters
 descriptionsequence of str
Description of the process.

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

setTimeGrid
(timeGrid)¶ Set the time grid of observation of the process.
 Returns
 timeGrid
RegularGrid
Time grid of observation of the process when the mesh associated to the process can be interpreted as a
RegularGrid
. We check if the vertices of the mesh are scalar and are regularly spaced in but we don’t check if the connectivity of the mesh is conform to the one of a regular grid (without any hole and composed of ordered instants).
 timeGrid
