Quote from Terrylee:
limitdown
A mixture model is an ensemble of functions which number, location and parameters have been chosen to represent the density in a data set. The functions (also called kernels) can be any kind of distribution. This applet shows an example where you can choose a Gaussian or a line as kernel. http://www.neurosci.aist.go.jp/~akaho/MixtureEM.html
Dtrader98
The paper that first caught my attention describes a âdynamicâ version of mixture models for which the out-of-sample extension is done through signal reconstruction.
http://www.aaai.org/Papers/IJCAI/2007/IJCAI07-468.pdf
In my case the data points represent the shifts of orders from one timeframe to the next so a natural out-of sample extension for it is the cycle in the larger time frame itself. Now that I know more about it, I think that Dynamic Mixture Models are able to learn the âquasi periodicâ occurrence of these shifts. I might go ahead with some experimentsâ¦
Stoxtrader
I make dlls in c++. This way I know what I do. But you are right I could also ask the MatLab community about it although I am mostly interested in getting some impressions from traders on the cycle shift phenomenon.
Hi Terry,
The paper looks interesting. I haven't read too much yet, but-- there is a thesis I think will interest you.
When I track it down, I'll PM you. It has to do with using hidden markov models, whereby each transition and emission probability is based on a mixture model that gets updated periodically (in this way it is constantly adapting). The results were pretty good, although the sample was pretty small, unfortunately.
Regarding regime switching using markov models, there is a well referenced econometrics paper under the author, Hamilton, if you want to look it up.