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myAntecedentProcess = ARMA(X_{0,t} = E_{0,t}, E_t ~ Normal(mu = 0, sigma = 1))
myCompositeProcess = class=CompositeProcess function=class=DynamicalFunction name=Unnamed implementation=class=SpatialFunction evaluation=class=AnalyticalNumericalMathEvaluationImplementation name=Unnamed inputVariablesNames=[x] outputVariablesNames=[y0] formulas=[2 * x + 5.0] antecedent=class= ARMA timeGrid=class=RegularGrid name=Unnamed start=0 step=0.1 n=11 coefficients AR=class=ARMACoefficients coefficients MA=class=ARMACoefficients noiseDistribution= class=Normal name=Normal dimension=1 mean=class=NumericalPoint name=Unnamed dimension=1 values=[0] sigma=class=NumericalPoint name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] state= class= ARMAState x= class=NumericalSample name=Unnamed implementation=class=NumericalSampleImplementation name=Unnamed size=0 dimension=1 data=[] epsilon= class=NumericalSample name=Unnamed implementation=class=NumericalSampleImplementation name=Unnamed size=0 dimension=1 data=[]
One realization=
[ y0 ]
0 : [ 0 0.63723 ]
1 : [ 0.1 5.70008 ]
2 : [ 0.2 4.28999 ]
3 : [ 0.3 7.8745 ]
4 : [ 0.4 6.62134 ]
5 : [ 0.5 6.58631 ]
6 : [ 0.6 4.05895 ]
7 : [ 0.7 5.52204 ]
8 : [ 0.8 0.419876 ]
9 : [ 0.9 2.43423 ]
10 : [ 1 2.37644 ]
My antecedent process = CompositeProcess(DynamicalFunction :
[x1,x2]->[x1^2,abs(x2)](TemporalNormalProcess(trend=[x0,x1]->[0.0,0.0], covariance=ExponentialModel(input dimension=2, amplitude=[1,1], scale=[0.2,0.3], no spatial correlation)))
My dynamical function = DynamicalFunction :
[x1,x2]->[x1^2,x1+x2]
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