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At one time I had to work with random processes, and the task was to get at least an approximate prediction of time series with 90% of the random process component. To turn a random process into a quasi random one, I invented a simple method, I multiplied the random process by a deterministic process with close frequency-time characteristics, a sine wave in the simplest case, but better to use a more complex signal. As a result, the predictability of the process went up by orders of magnitude.
Can you tell us more about it? If you are willing to share, of course.
It looks like I just did the BGS a little wrong I did it right - the analyser hangs up. I'll think about why tomorrow. I don't think spectroanalysis is a questionable method.
As far as I understand, the analyser is from Finware ?
Looks like I just did the BGS a bit wrong Did it right - the parser hangs. I'll think about why tomorrow. I don't think spectrum analysis is a questionable method.
As far as I understand the analyzer is from Finware ?
Yes
At one time I had to work with random processes, and the task was to get at least an approximate prediction of time series with 90% of the random process component. To turn a random process into a quasi random one, I invented a simple method, I multiplied the random process by a deterministic process with close frequency-time characteristics, a sine wave in the simplest case, but better to use a more complex signal. As a result the predictability of the process would go up by orders of magnitude.
Can you tell us more about it. If you want to share, of course.
Can you translate it into Russian, because you are inadequate
mql4-coding
Just don't leave this thread, there are people here who can help and many of the forum members are as good as maths professors.
The essence of the idea is simple, by creating covariances between a random process and a deterministic one, the resulting one inherits characteristics of both and becomes quasi random. And if we use several deterministic processes different in their characteristics for covariance and create covariances with one random process, and then select the most informative features from the resulting group using genetic algorithms, and then make a forecast using the signals obtained, and as a result subtract a deterministic process from the forecast obtained, then we will have a forecast of a random process in a residue, and the forecast accuracy will be much higher than in any attempt to make this forecast directly from the signals.
Kravchuk's system, in my opinion, is not remarkable in itself. It is built on the "standard" model of interpreting indicator signals.
Its peculiarity, which stimulated the development of the financial market analysis method in a way we already know, is the introduction of the nonstandard indicators (Numerical TFT).
But now it's clear that the author (and not only he) of this topic is looking for the more acceptable variant of the spectral analysis of time series. The topic is undoubtedly necessary and very interesting, but it requires sufficient skills in this area.
So we will have to wait for the learned men
The essence of the idea is simple, when creating the covariance of a random process ...