1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
|
myAntecedentProcess = ARMA(X_{0,t} = E_{0,t}, E_t ~ Normal(mu = 0, sigma = 1))
myCompositeProcess = class=CompositeProcess function=class=FieldFunction name=Unnamed implementation=class=VertexValueFunction evaluation=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[t,x,y0] evaluationImplementation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[t,x] outputVariablesNames=[y0] formulas=[t+0.1*x^2] gradientImplementation=class=SymbolicGradient name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[t,x] outputVariablesNames=[y0] formulas=[t+0.1*x^2] hessianImplementation=class=SymbolicHessian name=Unnamed evaluation=class=SymbolicEvaluation name=Unnamed inputVariablesNames=[t,x] outputVariablesNames=[y0] formulas=[t+0.1*x^2] 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=Point name=Unnamed dimension=1 values=[0] sigma=class=Point 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=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=0 dimension=1 data=[] epsilon= class=Sample name=Unnamed implementation=class=SampleImplementation name=Unnamed size=0 dimension=1 data=[]
One realization=
[ t y0 ]
0 : [ 0 0.475844 ]
1 : [ 0.1 0.112253 ]
2 : [ 0.2 0.212603 ]
3 : [ 0.3 0.506569 ]
4 : [ 0.4 0.465718 ]
5 : [ 0.5 0.56291 ]
6 : [ 0.6 0.622139 ]
7 : [ 0.7 0.706813 ]
8 : [ 0.8 1.32444 ]
9 : [ 0.9 1.06458 ]
10 : [ 1 1.17208 ]
future= [ t y0 ]
0 : [ 1.1 1.10082 ]
1 : [ 1.2 1.29916 ]
2 : [ 1.3 1.30194 ]
3 : [ 1.4 1.43138 ]
4 : [ 1.5 1.51985 ]
My antecedent process = CompositeProcess(FieldFunction :
[x1,x2]->[x1^2,abs(x2)](GaussianProcess(trend=[x0,x1]->[0.0,0.0], covariance=ExponentialModel(scale=[0.2,0.3], amplitude=[1,1], no spatial correlation)))
My dynamical function = FieldFunction :
[x1,x2]->[x1^2,x1+x2]
|
