Hidden markov models might be useful here. MacDonald and Zucchini's book titled Hidden Markov and other models for discrete-valued time series is a good start. Google Steven Scott's work on HMM too...
Peter Flom <[EMAIL PROTECTED]> wrote: >Hello > >In a job I have starting next week, the data will be a large set of >interrelated time series (I don't want to go into details because I am >not yet sure what is proprietary). There will be 19 time series for >each subject, all with a lot of points (thousands) and hundreds or >perhaps thousands of subjects. For each subject, the time series will >have multiple and probably some quite strong, relationships. > >Any references to classification (clustering, tree-based methods, >discriminant analysis, functional data analysis or what-have-you) of >such long time series would be welcome, preferably without TOO much math >background (I had 3 semesters of calculus, but am much more interested >in applications than theorems and proofs) > >Thanks > >Peter > >---------------------------------------------- >CLASS-L list. >Instructions: http://www.classification-society.org/csna/lists.html#class-l > ---------------------------------------------- CLASS-L list. Instructions: http://www.classification-society.org/csna/lists.html#class-l
