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myModel = class=ExponentialCauchy amplitude=[1] scale=[1]
mySecondOrderModel = class=SecondOrderModel implementation=class=ExponentialCauchy amplitude=[1] scale=[1]
myProcess = TemporalNormalProcess(trend=[x]->[0.0], covariance=AbsoluteExponential(input dimension=1, theta=[1], sigma=[1]))
is stationary? True
mean over 100 realizations = [ y0 ]
0 : [ 0 -0.0836695 ]
1 : [ 0.1 -0.0854839 ]
2 : [ 0.2 -0.130464 ]
3 : [ 0.3 -0.1401 ]
4 : [ 0.4 -0.145291 ]
5 : [ 0.5 -0.0687641 ]
6 : [ 0.6 -0.0651873 ]
7 : [ 0.7 -0.0923692 ]
8 : [ 0.8 -0.0571668 ]
9 : [ 0.9 -0.0140586 ]
10 : [ 1 0.0141645 ]
mean over 100 realizations = [ y0 ]
0 : [ 0 -0.102779 ]
1 : [ 0.1 -0.0417118 ]
2 : [ 0.2 -0.0484737 ]
3 : [ 0.3 -0.176127 ]
4 : [ 0.4 -0.155222 ]
5 : [ 0.5 -0.179764 ]
6 : [ 0.6 -0.106257 ]
7 : [ 0.7 -0.125425 ]
8 : [ 0.8 -0.0684988 ]
9 : [ 0.9 -0.0666161 ]
10 : [ 1 -0.0584345 ]
myCovModel = ExponentialModel(input dimension=1, amplitude=[1], scale=[1], no spatial correlation)
myProcess1 = TemporalNormalProcess(trend=[x]->[0.0], covariance=ExponentialModel(input dimension=1, amplitude=[1], scale=[1], no spatial correlation))
is stationary? True
mean over 100 realizations = [ y0 ]
0 : [ 0 -0.0630407 ]
1 : [ 0.1 0.0164204 ]
2 : [ 0.2 0.0215278 ]
3 : [ 0.3 0.0329501 ]
4 : [ 0.4 0.0392299 ]
5 : [ 0.5 0.0116844 ]
6 : [ 0.6 -0.0622486 ]
7 : [ 0.7 -0.119746 ]
8 : [ 0.8 -0.160072 ]
9 : [ 0.9 -0.162936 ]
10 : [ 1 -0.131856 ]
mean over 100 realizations = [ y0 ]
0 : [ 0 0.0577881 ]
1 : [ 0.1 0.00234506 ]
2 : [ 0.2 0.0754289 ]
3 : [ 0.3 0.14116 ]
4 : [ 0.4 0.15382 ]
5 : [ 0.5 0.0615315 ]
6 : [ 0.6 0.0757993 ]
7 : [ 0.7 0.10172 ]
8 : [ 0.8 0.0627895 ]
9 : [ 0.9 0.076767 ]
10 : [ 1 0.170701 ]
myProcess2 = TemporalNormalProcess(trend=[t]->[4.0], covariance=ExponentialModel(input dimension=1, amplitude=[1], scale=[1], no spatial correlation))
is stationary? True
mean over 100 realizations= [ outputVariable ]
0 : [ 0 4.25822 ]
1 : [ 0.1 4.21648 ]
2 : [ 0.2 4.17896 ]
3 : [ 0.3 4.1298 ]
4 : [ 0.4 4.15431 ]
5 : [ 0.5 4.16066 ]
6 : [ 0.6 4.08968 ]
7 : [ 0.7 4.02992 ]
8 : [ 0.8 4.03501 ]
9 : [ 0.9 4.01855 ]
10 : [ 1 3.94275 ]
myProcess3 = TemporalNormalProcess(trend=[t]->[sin(t)], covariance=ExponentialModel(input dimension=1, amplitude=[1], scale=[1], no spatial correlation))
is stationary? False
mean over 100 realizations = [ y0 ]
0 : [ 0 0.121181 ]
1 : [ 0.1 0.112961 ]
2 : [ 0.2 0.060845 ]
3 : [ 0.3 0.0787557 ]
4 : [ 0.4 0.100871 ]
5 : [ 0.5 0.016238 ]
6 : [ 0.6 0.0778269 ]
7 : [ 0.7 0.137006 ]
8 : [ 0.8 0.222522 ]
9 : [ 0.9 0.18291 ]
10 : [ 1 0.139098 ]
mean over 100 realizations = [ y0 ]
0 : [ 0 -0.0741775 ]
1 : [ 0.1 -0.110376 ]
2 : [ 0.2 -0.08856 ]
3 : [ 0.3 -0.0911573 ]
4 : [ 0.4 -0.116458 ]
5 : [ 0.5 -0.0786407 ]
6 : [ 0.6 -0.0424205 ]
7 : [ 0.7 0.0213822 ]
8 : [ 0.8 0.0247897 ]
9 : [ 0.9 -0.0408583 ]
10 : [ 1 -0.0519795 ]
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