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

class
RandomWalk
(*args)¶ Random walk process.
 Parameters
 origin
Point
Origin of the random walk.
 distribution
Distribution
Distribution of dimension equal to the dimension of origin.
 timeGrid
RegularGrid
, optional The time grid of the process. By default, the time grid is reduced to one time stamp equal to 0.
 origin
Notes
A random walk is a process where discretized on the time grid such that:
where and is a white noise of dimension .
Examples
Create a random walk:
>>> import openturns as ot >>> myTimeGrid = ot.RegularGrid(0, 0.1, 10) >>> myDist = ot.ComposedDistribution([ot.Normal(), ot.Exponential(0.2)], ot.ClaytonCopula(0.5)) >>> myOrigin = ot.Point(myDist.getMean()) >>> myRandomWalk = ot.RandomWalk(myOrigin, myDist, myTimeGrid)
Get a realization:
>>> myReal = myRandomWalk.getRealization()
 Attributes
thisown
The membership flag
Methods
Accessor to the object’s name.
Get a continuous realization.
Accessor to the covariance model.
Get the description of the process.
Accessor to the distribution.
getFuture
(*args)Prediction of the future iterations of the process.
getId
()Accessor to the object’s id.
Get the dimension of the domain .
getMarginal
(indices)Get the marginal of the random process.
getMesh
()Get the mesh.
getName
()Accessor to the object’s name.
Accessor to the origin.
Get the dimension of the domain .
Get a realization of the process.
getSample
(size)Get realizations of the process.
Accessor to the object’s shadowed id.
Get the time grid of observation of the process.
getTrend
()Accessor to the trend.
Accessor to the object’s visibility state.
hasName
()Test if the object is named.
Test if the object has a distinguishable name.
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.
setDistribution
(distribution)Accessor to the distribution.
setMesh
(mesh)Set the mesh.
setName
(name)Accessor to the object’s name.
setOrigin
(origin)Accessor to the origin.
setShadowedId
(id)Accessor to the object’s shadowed id.
setTimeGrid
(timeGrid)Set the time grid of observation of the process.
setVisibility
(visible)Accessor to the object’s visibility state.

__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

getDistribution
()¶ Accessor to the distribution.
 Returns
 distribution
Distribution
The distribution of dimension of the white noise.
 distribution

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.

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

getMarginal
(indices)¶ 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

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

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

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

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

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

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.

setDistribution
(distribution)¶ Accessor to the distribution.
 Parameters
 distribution
Distribution
The distribution of dimension of the white noise.
 distribution

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

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

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

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

thisown
¶ The membership flag