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From yikes aroni <yikesar...@gmail.com>
Subject Hidden Markov Models and time series - 2 questions
Date Wed, 22 May 2013 15:20:59 GMT
I'm not knowledgable of statistics nor data analysis, so please be
gentle! I am using Mahout to predict time series out of control state. I've
had a fair amount of success classifying with SGD and Adaptive regression
approaches but want to see if Hidden Markov Models can do a better job for
my purposes. I have two questions.

Question 1
I train the model using HmmTrainer.trainSupervisedSequence(). The hidden
state is the status: Out-of-Control (OOC) or Not-OOC for the next point in
time. Thus when i use HmmEvaluator.decode(model, observedSequence, false),
I look at the "state" associated with the last point in the
observedSequence and take *that* as my prediction of State at t+1. First of
all -- is this sensible? Or is there a better way to use the API to get a
prediction of State at t+1 given Observations 0 through t after training?

Question 2
 Once I get my prediction -- i.e., the state the model predicts will be
associated with the last observation in my observation sequence -- how do I
use the API to get the probability of a that predicted state being correct?
I've looked at various output from HmmUtils and HmmEvaluator, but not being
strong in my knowledge of HMM, i'm not sure which (if any) are what i need.
Ultimately, I want to be able to say something like "The predicted next
state of this time series is OOC with a confidence of 0.37".

thank you

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