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I tried to seek itfind one by changing the model and initial approximation (ln 15-16) atin this simple Python program:
The best result that I managed to get with the method described is shown in in the graph below:
.
DatafileThe datafile is here. I need to find a dependence of the second column from the first.
I think, I want to build a model which contains a periodical component, with "top-trend" and "bottom-trend" components, last two are independent.
I tried to seek it by changing model and initial approximation (ln 15-16) at this simple Python program:
The best result that I managed to get with the method described shown in in the graph:
.
Datafile is here. I need to find a dependence of the second column from the first.
I think, I want to build a model which contains a periodical component, "top-trend" and "bottom-trend" components, last two are independent.
I tried to find one by changing the model and initial approximation (ln 15-16) in this simple Python program:
The best result that I managed to get with the method described is shown in in the graph below:
The datafile is here. I need to find a dependence of the second column from the first.
I think I want to build a model which contains a periodical component, with "top-trend" and "bottom-trend" components, last two are independent.
Glen_b
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Here, firstly I try to find a periodic pattern ((p[0] + p[1] * np.exp(np.sin((np.pi / p[2]) * x + p[3]))**3)) while p[i] are variatingvarying parameters, then approximatingapproximate the remains of the first regression with second, polynomial regression.
The best result that I managed to get with the method described shown in in the graphgraph:
.
I'm pleased with how the fit is approaching the bottom part of the graph, but topsthe top parts I just do not like.
I think, I want to build a model which contains a periodical component, "top-trend" and "bottom-trend" components, last two are indipendentindependent.
Here, firstly I try to find a periodic pattern ((p[0] + p[1] * np.exp(np.sin((np.pi / p[2]) * x + p[3]))**3)) while p[i] are variating parameters, then approximating the remains of the first regression with second, polynomial regression.
The best result that I managed to get with the method described shown in the graph.
I'm pleased with how approaching the bottom part of the graph, but tops I just do not like.
I think, I want to build a model which contains a periodical component, "top-trend" and "bottom-trend" components, last two are indipendent.
Here, firstly I try to find a periodic pattern ((p[0] + p[1] * np.exp(np.sin((np.pi / p[2]) * x + p[3]))**3)) while p[i] are varying parameters, then approximate the remains of the first regression with second, polynomial regression.
The best result that I managed to get with the method described shown in in the graph:
.
I'm pleased with how the fit is approaching the bottom part of the graph, but the top parts I just do not like.
I think, I want to build a model which contains a periodical component, "top-trend" and "bottom-trend" components, last two are independent.
