Bayesian regression - Has anyone made an EA using this algorithm? - page 43
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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.