Machine learning in trading: theory, models, practice and algo-trading - page 1993
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drunk probably... On the plus side, absolute is relatively greater than one, less than absolute is relatively less than one. The logarithm of absolute differences leads to relative differences. They just don't teach unit circle in schools nowadays. My wife sometimes works on this subject.... From there I know how difficult it is to teach the unreasonable to the unreasonable.....
drunk apparently... On the plus side, absolute is relatively greater than one, less than absolute is relatively less than one. The logarithm of absolute differences leads to relative differences. They just don't teach unit circle in schools nowadays. My wife sometimes works on this subject.... from there I know how hard it is to bring reason to the unreached.....
I wrote a simple thing to the man, without squares or anything else.
don't write too much.
I just wrote to the man in simple terms) without squares and stuff.
Don't write a word too much.
It's hard to understand people you don't know straight away. Sometimes a couple of letters are not enough to understand what the proger wants to say, and with the coder even harder. Well... I'm going... to the remote....... sorry... if anything .....
Yes, it is difficult to understand strangers in half a word. Sometimes a couple of letters are not enough to understand what the proger wants to say, and with the coder even more difficult. Well... I'm going... to the remote....... sorry... if anything .....
I wrote that it's hard to imagine a model that would at least show inverse dependence, i.e. learning in reverse, on a trace
I do not understand then about the inverse dependence. if it is a constant dependence on past data, then what is complicated here. what is the inverse dependence then.
I do not understand then about the inverse dependence. if it is a constant dependence on past data, then what is complicated here. what is the inverse dependence then.
Correlation of initial and predicted series
Then the model works the other way around. I realized that the question was about the increments. An inverted model, or multiplied by minus one without moduli. Although of course in the NS layers it may not be so straightforward.
Then the model works the other way around. I understood that the question was about increments. Inverted model, or multiplied by minus one without considering modules. Although of course in the NS layers everything may not be so unambiguous.
It's just that this metric can return negative values, yes. But this almost never happens in practice. Then you can take the value as a relative value, what's the problem. We're not great mathematicians here.
Negative correlation between the raw and predicted data is the wrong model. Of course it's a rare case. And the correlation between them of course is not absolute, and rather not even relative, there on the layers depends on what order diff, acceleration of what order type.
Negative correlation between the original and predicted data is the wrong model. Of course, this is a rare case. And the correlation between them of course is not absolute, and rather not even relative, it depends on the layers of what order diff, acceleration of what order type.