Machine learning in trading: theory, models, practice and algo-trading - page 2562
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Offtop, but fun
https://www.youtube.com/watch?v=_Aow6P3oBAg
Hi!
Are you the TechnoShaman?
Been signed up for a few years now.
Hi!
Are you the TechnoShaman?
Signed up for a few years now.
Nah, I don't know about him.
I am slowly getting into wavelet decomposition.
I came across this scheme, on the difference between ordinary decomposition and batch decomposition.
I already highlighted it myself, for better visibility.
They say that batch decomposition gives more accurate results than ordinary decomposition.
Perhaps you, too, would be interested to know about it.
I am slowly getting into wavelet decomposition.
Why don't you like MGC or SSA?
What's wrong with MGC? Or SSA?
While it is interesting to feel the wavelets.
SSA is a crawler?
I think there were indicators based on it a long time ago, I was not impressed.
If those who understand it haven't reached the required result.
I don't know what it is.
While it is interesting to feel the wavelets.
Is SSA a caterpillar?
I think there were indicators based on it a long time ago, I was not impressed.
If those who understand it did not reach the required result.
I don't know what it is.
Yes, it's a caterpillar.
MGC is PCA.
I think it makes no difference how to decompose it, the main thing is what to do with it afterwards...
I am slowly getting into wavelet decomposition.
I came across this scheme, on the difference between ordinary decomposition and batch decomposition.
I have already highlighted it myself, for better visibility.
They say that batch decomposition gives more accurate results than ordinary decomposition.
Perhaps you, too, would be interested to know about it.
Some years ago, I looked through a bunch of books on wavelets, and now I'm more interested in how to count them quickly. These diagrams are actually misleading. The diagrams show a breakdown into the LF and HF components. This is what will be after the wavelet decomposition. But from the schematics, it seems that wavelets are LF filters, when in fact wavelets are bandpass filters with some properties that mathematicians like.
What is described above does not include a liftoff circuit. It's pretty interesting stuff, but I didn't get into it deeply.
Some years ago I looked through a bunch of books on wavelets, now I'm more interested in how to count them quickly. These diagrams are actually misleading. The diagrams show a breakdown into the LF and HF components. This is what will be after the wavelet decomposition. But from the schematics, it seems that wavelets are LF filters, when in fact wavelets are bandpass filters with some properties that mathematicians like.
What is described above does not include a liftoff circuit. It's pretty interesting stuff, but I haven't delved deeply into it.
Bandwidth it probably becomes after extraction of coefficients, a certain node,
setting a penalty threshold and further noise reduction, compression.
I'm still studying it too. The point is that wavelets are applicable to both one-dimensional signal and matrix data.
Maybe lifting is just applicable to matrix transforms. So far, I want to understand the variants of application of different approaches.
It's not about finding a randomly successful model, it's about increasing the probability of making that model successful.
Then you need to be more specific.