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Originally Posted by pvpn One thing I have on my list is to make (or use) an algo to automatically discover states and transitions. My guess is that there are too many states for a human to try to identify them and connect them with the proper links. |
You might want to read about hidden markov models (which I think are a bit too simplidfied to do the actual trick but they are kind of easy to variate). There is a lot of interesting research made on spam filtering (bayesian filtering can be used too when you decide what attributes you want to study) and context discovery (directional network graphs for example) that is quite relevant to state discovery of "hidden" states of any kind of data.
I personally don't use state data per se but my genetic algos
can use it if they want to. Basically I think in some methods state is important (especilly if you can anticipate state transition in any meaningful way) but for some other methods state is not as important.
I'm not sure if this is a bit offtopic to this thread so my thread is open for exactly this kind of discussion if anyone is interested.
http://www.forexfactory.com/showthread.php?t=167720