The Sultonov Regression Model (SRM) - claiming to be a mathematical model of the market. - page 4

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yosuf:
We'll get to Fourier as well, wait for it.
throw me a link to read about the magic model, because google knows about televisions and rsm vibration plates, I don't think that's it.
 
ivandurak:
Let's take a price series. describe it with a polynomial, a neural network or a Fourier. we'll get a model that describes this series with almost any accuracy. but this model will not be predictive for the next bar, the same heads and tails. perhaps it is better to build a market state model that determines the trend and flat in the early stages of their emergence. although there are also many market states, but if we approach it from the profit perspective, the set is most likely limited to 5 - 10 states.
Have you yet to be convinced of the predictive ability of the model when it has perfectly predicted the behaviour of the tangent many moves ahead? Believe me, no function is capable of such a thing except the tangent itself, and RMS has calculated it. Try doing a similar thing with the "polynomial, neural network or Fourier" you mention. Doesn't the behaviour of the tangent resemble the price reaction to a news release?
 
yosuf: We'll get to Fourier as well, wait for it.
That's it, kaput Fourier: yosuf is here!
 
ivandurak:
throw me a link to read about the magic model, google tvs and rsm vibratory plates, I don't think that's it
https://www.mql5.com/ru/articles/250
 

Yusuf, try using your model to continue at least ten steps into the next row:

101101100011101100011101100010010011100010011100010011101101100010010011100010011101101100

p.s. This series is not random. I will reveal the algorithm and further values of the series after I receive your prediction.

 
anonymous:

Yusuf, try using your model to continue at least ten steps into the next row:

101101100011101100011101100010010011100010011100010011101101100010010011100010011101101100

p.s. This series is not random. I will reveal the algorithm and further values of the series after I get a prediction from you.

Now we will try:

For now on the first 30 points:

 
Mathemat:
That's it, kaput Fourier: yosuf is here!

Fouriertrousers turn into elegant shorts
 
Demi:

All the basic assumptions of correlation and regression theory are based on the assumption that the data under study is normally distributed. Do your inputs (price) have a normal distribution?


What does this have to do with the normality or non-normality of the distribution, and what does it have to do with the distribution at all?
 
Integer:

What does this have to do with normality or nonnormality of the distribution, and what does it have to do with distribution in general?

Don't cling. The man learned the right words. Achievement? Achievement. Now all that's left to learn is to use them in the right combination and in the right place. It's all right. We need to keep it up.
 
yosuf:
The RMS will find the most appropriate dependency, not a dislocation.
The fact will determine what was yesterday...and not even now...
what will happen tomorrow will never be known )))...
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