The market is a controlled dynamic system. - page 61
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You are mistaken. In reality for adaptation purposes such an assumption is not necessary. But in the case of a non-adaptive model one is forced to make some assumptions, postulate them in order to get ground beneath one's feet.
I stand by my opinion: when building a solver, one cannot do without nonparametrics.
It is also possible to self-deceive here: many adaptation methods are designed on the assumption that the noise is Gaussian, so implicitly this point is included in the model anyway.
The difference is very significant.
An nth-order astatism ensures zero system error up to the (n-1)-th error coefficient.
That is, with acceleration control, the error will be in acceleration and the errors in velocity and position will be zero. In this case any accumulation of errors is out of the question.
You forget that the input process is essentially stochastic (and that both the useful signal and the noise are random) and actually, strictly speaking, in theory even undifferentiated. It is not some second-order curve that a system with second-order statism will track. Our process has an infinite number of non-zero derivatives, and their value does not decrease with increasing order, quite the contrary. Therefore, in terms of the astatism this problem is, alas, unsolvable.
Here is the data for the first 10 EURUSD derivatives:
... ATS diagram will be available a little later ...
This approach may well end up being workable. But let's wait for the schematic. A structural diagram is good because not only does it allow you to see the whole problem, without going into fine detail, but it also allows you to see the fine points.
Incidentally, an energy function can be entered regardless of the linearity/non-linearity of the equation being described.
P.S. I see your schematic and pictures. That was quick, you made it up...
Simulink, what's there to cook... It took me longer to remember the quantile conversion, so that I could write the pulses...)
I'll stick to my opinion: you can't do without non-parametrics when building a solver.
It is also possible to be self-defeating here: many adaptation methods are calculated assuming that the noise is Gaussian, so implicitly this point is included in the model anyway.
It's a far cry from the same thing. But it's not essential either.
An explicit fit to a particular distribution for n(t) will inevitably skew s(t).
You forget that the input process is essentially stochastic (and that both the useful signal and the interference are random) and actually, strictly speaking, in theory, not even differentiable. It is not some second-order curve that a system with second-order statism will track. Our process has an infinite number of non-zero derivatives, and their value does not decrease with increasing order, quite the contrary. Therefore this problem is, alas, unsolvable in terms of statisms.
I do not forget about it and remember both the nature of the process and the nature of the disturbance.
But to draw such an analogy -- a curve of the second order and the astatism of the system of the second order -- to put it mildly, it is not necessary. And besides, the problem is not solved "in terms of astatism", for in such a way the problems cannot be solved. Astatism is a property of the system.
Here is the data for the first 10 EURUSD derivatives:
What is it? How was it counted? And why was it counted?
Was an intermediate smoothing performed or are the differences just piled up here?
But if we perform intermediate filtering as it should be done, then on the sample N=1024 we will obtain the following values
GBPUSD Daily
But it's just a saying...
But if we perform intermediate filtering, as it should be done, we will obtain the following values for the sample N=1024
GBPUSD Daily
But it's just a saying...
Why 1024?
There is a limit to the number of bars in the window. I don't need more, a thousand is enough.
Limit the number of bars per window. I don't need more, a thousand is enough.
But, there are 1024.
But it's 1024.
Yeah, 1024.
Yeah, 1024.
Got it.