Machine learning in trading: theory, models, practice and algo-trading - page 2821

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that's the probability of a cluster, geometrically interpreted.

you've been told you're delusional and don't know what you're talking about. No one has changed since then.

 
Maxim Dmitrievsky geometric interpretation
Well.... and you equate this geometric probability of proximity, which is in clusters, with the normal probability, which is in hmm, and say that they work the same way.

Because clusters and hmm work the same, according to you....

If that's true, and it is, then the verdict is arsehole)))
 
mytarmailS #:
Well.... and you equate this geometric probability of proximity, which is in clusters, to the normal probability, which is in hmm, and say that they work the same.

Because clusters and hmm work the same, according to you....

If this is true, and it is, then the verdict is arsehole))))
Everyone has already realised that and even stopped responding to you. Unfortunately, you dared to comment on my post, had to trash you once again.
Keep on rambling on from here. You can come back later for another round of intellectual bashing. I think this topic has been exhausted, but you haven't got to it yet, like a giraffe. That's normal for a ptu.
 
Ohhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh.
Ruined so Ruined...
What a profound argument.)
SHAME ON YOU...
Neptushnik)))))))))))))))))))))))))))))
 

Curious article.

Translation of abstract

This paper compares the forecasting accuracy of neural networks and conditional heteroscedastic models such as ARCH, GARCH, GARCH-M, TGARCH, EGARCH and

IGARCH, for forecasting a range of exchange rates.Multilayer perseptron networks (MLP) and the

radial basis function (RBF) networks with different architectures and conditional

heteroscedastic models are used to forecast five time series of exchange rate. The results show

that both neural network and conditional heteroscedastic models can be effectively used for forecasting

for forecasting. RBF networks perform significantly better than MLP networks in the neural

network case study. IGARCH and TGARCH perform better than other conditional heteroscedastic

models. The performance of neural networks

in predicting the exchange rate is better than conditional heteroscedasticity models. It is shown that the neural network can be effectively

used for estimation of conditional volatility of exchange rate series and implied volatility of options N

volatility of NIFTY options. It is found that the neural network outperforms conditional heteroscedastic

models in out-of-sample forecasting.

 
The advantage of arche-like is the minimum number of parameters, probably in relation to the number of weights neurons have. RBF has fewer weights than mlp too. That's how you count it though.
 
Maxim Dmitrievsky #:
The advantage of arche-like is the minimum number of parameters, probably in relation to the number of weights neurons have. RBF has fewer weights than mlp too. Though that's how you count it.

Archie models non-stationarity, and in quite a bit of detail.

MO models, probably neuroncs as well, exploit the idea of "history repeats itself" by looking for patterns.

Does the article imply that the pattern-finding path is more promising than modelling non-stationarity?

 
СанСаныч Фоменко #:

archi modelling nonstationarity, and in quite a lot of detail.

MO models, probably neuronics as well, exploit the idea of "history repeats itself" by looking for patterns.

Does the article imply that the pattern-finding pathway is more promising than modelling non-stationarity?

Modelling non-stationarity implies modelling volatility, as I understand it. Without the direction of trades. In that respect, patterns or shifting average increments are more promising for directional trading. Haven't looked at the article yet.

I would even give up trades in different directions, e.g. Eurobucks for the last 10 years should be stupidly sold periodically, without buying. There any purchases will introduce more errors in the models than sales.
 
Maxim Dmitrievsky #:
Modelling non-stationarity implies modelling volatility, as I understand it. Without directional trades. In this respect, patterns or shifting average increments are more promising for directional trading. Haven't looked at the article yet.

I would even give up trades in different directions, for example Eurobucks for the last 10 years should be stupidly sold periodically, without buying. There any buying will introduce more errors in the models than selling.

I agree.

In our terminals trades sign. What is volatility is not clear at all.

But if one is forecasting the absolute value of an asset, that's another matter. Volatility is risk, which is crucial in predicting the value of an asset.


Probably something like that.


So will forget about the garchas.

 
Maxim Dmitrievsky #:
Modelling non-stationarity implies modelling volatility, as I understand it. Without directional trades. In this respect, patterns or shifting average increments are more promising for directional trading. Haven't looked at the article yet.

I would even give up trades in different directions, for example Eurobucks for the last 10 years should be stupidly sold periodically, without buying. There any buying will introduce more errors in the models than selling.

I don't think that now is the time to determine what is better, to determine the absence of a signal in chaos by comparing the environment with chaos, or the presence of a signal by distinguishing it from the chaos. Neither is it better to predict or determine state. It's a time of experimentation.

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