Machine learning in trading: theory, models, practice and algo-trading - page 3028
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convolutions, convolutional nuclei
convolutional kernel transform
Got it. But something in imagination does not draw how they can be connected with quantisation at all to be mutually substituted. The history is rather pulled up by them. I plan to try them later.
You should have said so right away.
I don't know how to be more specific - I think Maxim understood.
" In binary form. The column is the number of the rule, and the value is "1" - the rule worked and "0" - the rule did not work. Well, and the target as on the main sample. "
Got it. But something in imagination does not draw how they can be connected with quantisation at all, that would be mutually replaced. It's more like history is being pulled up by them. I plan to try them later.
study
https://www.arxiv-vanity.com/papers/1910.13051/
https://www.youtube.com/watch?v=1BRYrnvMhyI
The article contains many references to other state-of-the-art methods of time series classification, methods of signal and pattern extraction.
There is nothing about inefficiencies, but this is, as they say, homework
Well, I can't yet figure out how to implement profit maximisation into the same bousting, for example.
I am doing something, of course, but I would like to hear other informative opinions on the topic.
how are you doing with boosting and profit maximisation?
study
https://www.arxiv-vanity.com/papers/1910.13051/
https://www.youtube.com/watch?v=1BRYrnvMhyI
The article contains many references to other state-of-the-art methods of time series classification, methods of signal and pattern extraction.
There is nothing about inefficiencies, but this is, as they say, homework
Yes, the theory of creation is clear. There is a question of rationality in my mind, and generation of different variants. The plan is to make a generator and a tester with quantisation to evaluate the efficiency of each instance of the convolution kernel. Later - the first priority task - prediction of data drift in a particular predictor. Without solving this task, my interest in everything drops.
The theory of creation is clear enough. It's a question of rationality for me, and generation of different variants. According to the plan, I will make a generator and a tester with quantisation to evaluate the efficiency of each instance of convolution kernel. Later - the first priority task - prediction of data drift in a particular predictor. Without solving this problem, my interest in everything drops.
"Quantisation" highlights some of the properties of the fiche, as I understand it. A convolution does the same thing. It turns out to be buttery buttery.
Reconciliation on time series aggregates information about past values of predictors (it is possible to take those that were in the sample and those that were not), and quantisation evaluates the success of this action.
Time series convolution aggregates information about the past values of predictors (it is possible to take those that were in the sample and those that were not), and quantisation evaluates the success of this action.
What is quantisation?)
In the context I'm referring to, it is a piecemeal evaluation of a range of data in order to identify a piece (quantum segment) whose probability of belonging to one of the classes is x per cent greater than the average over the whole range.
In the context I'm referring to - a piecewise evaluation of a range of data in order to identify a chunk (quantum segment) whose probability of belonging to one of the classes is x per cent greater than the average over the whole range.