I am looking into using some simple (HMM) Hidden Markov Model libraries I have for Java. ( see http://www.elitetrader.com/vb/showthread.php?s=&threadid=30307 ) I was wondering if anyone has any experience using HMMs, with the belief that the number of underlying states for data-generating process is possibly finite (or with small variations) and context-sensitive - i.e denominated "regimes" for contexts in time series data ? I don't particularly believe in modelling OHLC data, but rather pre-processed/aggregated data, and identifying patterns within ranges of aggregated data as a basis for decisions/signals. The same goes for Bayesian Networks; any experiences using own software or doing your own models in applications ? I am interested in what ideas are plausible for good aggregated data as basis for training/learning networks. Also, I'm exclusively focusing on ES futures learning, but may be inclined to believe that other data series might have some influence in forming identifyable/taggable patterns/states when combined with ES data. Other than the usual bar-/candle-data formations and common TA (lagging) indicators as Stochastics etc, have you tried identifying new types of patterns for use in time series input for AI/learning models ? I also have some Fuzzy logic libraries as well as expert systems/rules-based and Prolog stuff being interfaced with my own experimental application. I might do a Matlab connection for accessing some othe libraries too. It all depends on available time and perceived usefulness of the additional tools. Price prediction is futile in my view, but identifying patterns and conditions may be worthwhile. BTW here is a good text on Bayesian Networks by Richard E. Neapolitan - "Learning Bayesian Networks" from the authors homepage http://www.neiu.edu/~reneapol/cooper.pdf .