From theory to practice - page 71

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Alexander_K2:

No, I still have to say goodbye, but in a more abbreviated form, or else they'll delete it again...

Most importantly!

The processes of price returns, i.e. x(t)=Ask(t)-Ask(t-1) and y(t)=Bid(t)-Bid(t-1) are STABLE.

Use non-parametric methods in your analysis.

Whoever figures it out has some amazing discoveries waiting for him.

Is it ok?

Respectfully,

Alexander and Schroedinger's cat from Hilbert space.

We'll be sure to come back with the results, because that's what everyone is waiting for, isn't it?


1)https://www.mql5.com/ru/forum/219894/page5#comment_6193436


2) https://www.mql5.com/ru/forum/219894/page6#comment_6196243

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Vladimir:

You have calculated the RMS correctly. However, see how much it would be if n=1. You'll wonder what kind of nonsense this is. The name "n - volume of statistical population" is very vague, usually they write that n is the number of elements in the sample. Then the RMS according to this formula can't be calculated if there is only one element. That's why the square of the RMS is called a "biased" variance estimate. There is also an unbiased one, where n is n1-1 instead of n in the denominator. The square root of the unbiased variance estimate is called the standard deviation.

The nature of this conflict is that one item has one degree of freedom. If many-many features are defined from a small number of data, they become dependent on each other. In this case the arithmetic mean is included in the RMS calculation. So to speak, one degree of freedom has already been used. The "strange" behaviour of the denominator of the standard deviation is just to say that both the mean and the spread cannot be determined from a single element. It can be seen that the standard deviation is always greater than the standard deviation by a factor of [n/(n-1)]^0.5. However, if the number of elements in the sample is large, you can forget about it, because it is not much. When n=100, it is (100/99)^0.5=1.005, which is half a percent. Moreover, if we know for sure that the RMS tends steadily to some value.

This is where the tricky part comes in. "RMS tends to", i.e. the laws of large numbers work. If the real phenomenon being measured actually has this stability. In other words, the basic hypothesis of probability theory is fulfilled - the relative frequency of an event tends to some value as the number of events increases. This is also called "statistical stability". If it does not exist, all classical probability theory is inapplicable to the phenomenon. This difference is discussed in the huge quotes from Oleg avtomat, which start fromhttps://www.mql5.com/ru/forum/221552/page58#comment_6191471 and onwards. They are hard to read. In my opinion, it is much more fun to view the presentation of Gorban's report with pictures and graphs. It will create a more optimistic and constructive mood, such as this phrase:

"It has been shown that ocean swell, traditionally regarded as a pronounced destabilizing factor, can improve the performance of hydroacoustic stations."

Even for exchange rates, the author has walked around looking for the phrase "Averaged over 16 decades, the statistical instability parameter (continuous curve) and the range of variation of this averaged parameter, defined by RMS, (dashed curves) for the Australian dollar (AUD) quote against the US dollar (USD) for 2001 (a) and 2002. (б)".

I attach the presentation, and for those who want more sources, here's a list of presentations, sometimes with file addresses, from the list "Archive of past "Image Computer" seminars http://irtc.org.ua/image/seminars/archive from 2002-2017. Gorban has up to a dozen monographs on developments in "hyperrandom" phenomena:

I.I. Gorban THEORY OF HYPERSLUTE Phenomena. Theory and Practice. Section 7. System Analysis.
I.I. HURBAN I HYPERRANDOMNESS KIEV NAUKOV DUMKA 2016. - 288 p. ISBN 978-966-00-1561-6


you tell me this.

How is the cattle better than the simple average deviation?

Why does it always apply?

 
Максим Дмитриев:

you'd better tell me this.

is better for the simple average deviation?

why does it always apply?

If you give me the formula for calculating the"simple average deviation", maybe I can tell you. Otherwise, I just don't know what it is.

Or you can tell me. Just so that everyone who has done the calculation according to your story has the same calculation result.

 
Vladimir:

If you give me the formula for calculating the "simple average deviation", maybe I can tell you. Otherwise, I just don't know what it is.

Or you can tell me. Only to have the same calculation result for everyone according to your story.

Average the scatter distances to the mean.
That's probably what it means. The range of values can be above and below the average. Correspondingly with + and -. In sko, the square removes the dependence on the sign. What he means is not to square each value to the average, but to simply take modules and average them. Without roots and squares.
 
ILNUR777:
Average the distances of the scatters to the mean.
This must be what was meant. The range of values can be above and below the average. Correspondingly with + and -. In Sko, the square takes away the dependence on the sign. What he means is not to square each value to the average, but to simply take modules and average them. Without roots and squares.


This also applies.
 
Yuriy Asaulenko:


That's being applied too.
I am aware of that. But the question he asked you is quite specific and different).
 
Vladimir:

If you give me the formula for calculating the "simple average deviation", maybe I can tell you. Otherwise, I just don't know what it is.

Or you can tell me. Just so that everyone who has done the calculation according to your story has the same calculation result.


Well, it's just the sum of all the deviations divided by the number of deviations.

 
ILNUR777:
Average the distances of the scatters to the mean.
This must be what was meant. The range of values can be above and below the average. Correspondingly with + and -. In Sko, the square takes away the dependence on the sign. What he means is not to square each value to the average, but to simply take modules and average them. Without roots and squares.

Uh-huh

 
ILNUR777:
Average the distances of the scatters to the mean.
This must be what was meant. The range of values can be above and below the average. Correspondingly with + and -. In Sko, the square takes away the dependence on the sign. What he means is not to square each value to the average, but to simply take modules and average them. Without roots and squares.
Should we read "average absolute deviation from the arithmetic mean"? There are also different ways of calculating both "averages". For example, in this forum we often talk about the geometric mean. We'll get tired of guessing, so let the person tell us what it is.
 
Максим Дмитриев:

well just the sum of all the outliers divided by the number of outliers

You get 0).
1...646566676869707172737475767778...1981
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