Bayesian regression - Has anyone made an EA using this algorithm? - page 43
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Stationarity is the property of a process not to change its characteristics over time.
What characteristics specifically?
Dispersion
and that's it?
In a broad sense also the MO and the distribution function
Then, broadly speaking, if MOE, a stochastic would suffice. No?
Non-stationary data are not predicted by time series models. Neither statistical models (regression, autoregression, smoothing, etc.) nor structural models (NS, classification, Markov chains, etc.).
Only subject area models.
I cannot agree with you about classification.
The problem of non-stationarity is not seen there at all. Models on nominal (categorical) data are quite acceptable. Non-stationarity has nothing to do with nominal data at all. Moreover, converting random variables to nominal, e.g. RSI to levels, is very beneficial to the results.
There follows non-stationarity, a problem that is fundamental to any modelling - overfitting (overfitting) of the model. And to solve the problem of overfitting one has to seriously deal with predictors.
I cannot agree with you about the classification.
There is no problem of non-stationarity there at all. Models on nominal (category) data are perfectly acceptable. Non-stationarity has nothing to do with nominal data at all. Moreover, converting random variables to nominal, e.g. RSI to levels, has a very favourable effect on the results.
There follows non-stationarity, a problem that is fundamental to any modelling - overfitting (overfitting) of the model. And to solve the problem of overfitting one has to seriously deal with predictors.