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I did, but it didn't work.
That's strange... Okay, I'll take another look at this thread - I'll let you know later.
which is not mentioned in the official documentation.
Maxim, can you compare where the learning speed is faster, in python or in the console version of CatBoost?
Strange... All right, I'll look at this topic again - I'll write back later.
At first I spoke out of recollection and was wrong.
Checked just now with cross validation. Time still gives 1-2% gain on new data. While the total preponderance of successful predictions over unsuccessful about 5% at TP = SL. Ie from 10 to 30% of the contribution to success comes from time.
However, this is a test on a 2-month piece of history. Things may change at another site.I feed the time as cosine and sine. This was discussed here https://www.mql5.com/ru/forum/212376#comment_5983502
Simply as 0...5(day of week) or 0...23 (hour) or 0...59 (minute) can also be fed, but as a categorical variable.
Sine and cosine are already numeric, any algorithm will accept.
there is a relatively new trend called active learning. It can self-sample the data in the best way. It seems to be suitable for my approach (random sampling). I haven't got into it yet.
https://libact.readthedocs.io/en/latest/overview.html
https://medium.com/towards-artificial-intelligence/how-to-use-active-learning-to-iteratively-improve-your-machine-learning-models-1c6164bdab99
As I go along, the frequencies and possibly the phases are floating... The amplitudes are holding...
Here is the forecast for 500 points of the fitted model on the history of 10k of 4 harmonics
We can see that the forecast is relevant for all 500 points, but the frequencies are drifting, and they are drifting according to an incomprehensible algorithm
And this is an illustrative example, it may be even worse.
Have you tried it on the increments?
" One of the useful properties of a series of increments is the greater stability of the spectrum compared to the original process. ".This is Goodman writing.
I feed the time as cosine and sine. This was discussed here https://www.mql5.com/ru/forum/212376#comment_5983502
Simply as 0...5(day of week) or 0...23 (hour) or 0...59 (minute) can also be fed, but as a categorical variable.
Sine and cosine are numeric, any algorithm will accept.
Can you post the function for converting time to sine/cosine? I would also try this method. In the article I published, the hour number was a significant predictor there. I wonder if this method is suitable for wooden models or more for neural networks.
Have you tried it on the increments?
" One of the useful properties of a series of increments is the greater stability of the spectrum compared to the original process. ".This is Goodman writing.
no