Machine learning in trading: theory, models, practice and algo-trading - page 2044
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You all believe )))
If I understood correctly from the video, there is a function/library, which searches for features in the convolutional network, i.e. ready-made patterns by which patterns/predictors should be found - I wonder what is expected to be found there, how this mask was made - what is the logic, do you know by any chance?
it picks up the mask from the weights by itself when training for a given pattern length
It just passes a window on the feature vector, the window is smaller than the number of features. A convolution takes place.
You all believe ))))
our motto is invincible
I'm not sure that our inputs are suitable for this network - everything looks smooth in the pictures.
Any data, any time series
This is the technology of the present and the future of the typeit itself picks up a mask from the weights during training for a given pattern length
It just passes a window on the feature vector, the window is smaller than the number of features. The convolution takes place.
It seemed that the speaker talked about some ready-made mask solutions, hmm, maybe I got it wrong.
It seemed that the speaker was talking about some kind of ready-made mask solutions, hmm, I must have misunderstood.
a mask is a sliding window
any data, any time series
These are present and future technologies of the typeMaybe a vector will show movements, but aren't fluctuations more important to us? Maybe we need to look for points with potential strong outliers?
Maybe the vector will show movements, but aren't fluctuations more important to us? Maybe we should be looking for points with potential strong outliers?
What?
You all believe )))
20 kliks.
see what's going on.
Epoch 820 train err: 0.4057188332080841 tst err: 0.4114921987056732
you have to write a tester to see
I ran in the tester, the left column expectation. Clearly there is a dependence on the day of the month, and catbust put 0.
Outputs are more diverse.