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You can only mix within a sample, if you mix two samples, you are denying that the market is changing.
Can't you see the logic again?
How can you prove that the market is changing? How long does this process take? Or is it constantly changing?
How can you prove the changeability of the market? How long does this process take? Or is it constantly changing?
That's it, the delightful arguer is on.
And what sample size should be taken to determine stationarity/non-stationarity?
According to you a pattern lives no longer than the lifetime of the sample change, but what if I have a pattern in my sample that repeats for 8 years? What is that, an anomaly, or the patterns are not all changing or the patterns identified in a small area are erroneous and due to other factors?
Each predictor individually is a numerical sample, so why not estimate them individually and average the result?
This works only in the case of independent features, and since they are counted at the same price, it is not possible. In the case of dependence everything is much more complicated - we can take copulas as an example, where univariate distributions are always the same uniform, but bivariate distributions can be very different.
Perhaps we should find those variants that will give the best results in terms of identifying the belonging of segments to a particular group and the efficiency of training on a grouped population.
You have an appetite for heavy enumeration calculations) We will have to add (to the already considerable amount of enumeration) enumeration by feature types and, probably, by feature parameters.
Nevertheless, it seems to me that there is a rational grain in your approach, there is something to think about.
Didn't I write that the idea is to compare samples (training and application), that if your theory is correct, the sample will cease to be similar as it increases, and in order to understand this we need criteria for assessing its change, which are derived from the methods of assessing similarity?
And what sample size should be taken to determine stationarity/non-stationarity?
According to you, a pattern does not live longer than the lifetime of a sample change, but what if I have a pattern in my sample that repeats for 8 years? What is that, an anomaly, or the patterns are not all changing or the patterns identified in a small area are wrong and due to other factors?
Different models but similar, different and not similar how do they differ? The bifurcation point will not necessarily lead to a change of the model, it is possible to mark the same areas visually manually, but there is no predictive part at the end, the goal is to find the minimum length of the sample, which confirms the state or compliance of the model.
Complexity of the model, here of course there is also a contradiction, a simple model will not describe a sufficiently necessary long section, but will be repeated, a complex model can describe a sufficiently necessary section in length, but may be unique. As always something in the middle is needed))))))
Complexity of the model, here of course there is also a contradiction, a simple model will not describe a sufficiently necessary long section, but will be repeated, a complex model can describe a sufficiently necessary section in length, but may be unique. As always something in the middle is needed))))))
It's an unimaginable mess: everything is mixed up - horses, people.....
We can distinguish two types of models
1. Based on machine learning ideas.
2. Statistical models, which are fundamentally more widely used in financial markets.
MO
As it seems to me all MO algorithms have one goal - to find some number of patterns. In this case, a pattern is a string with a teacher value and feature values. There is no value of rows next to each other! The number of such patterns can be looked up in RF, from about very often with 50 trees the fitting error changes very little. More than 150 trees is meaningless. That's the diversity of financial markets.
And we should reason about the lifetime of these trees, which (lifetime) is determined by the stability of the connection between the features and the teacher. I.e. we should deal with the connection between traits and teacher.