Hello,
I consider using Mahout's implementation for Hidden Markov model (HMM)
for prediction, but I want to clarify some important questions before
using it:
1. I've read some literature about HMMs and in some sorces is written,
that HMMs can also handle continiuous values as input (and not only
discrete values). Can Mahout's implementation also handle such values ?
My input data is only continious.
2. Can Mahouts HMM have many hidden markov chains? I don't know, if I
use the right terminology, but what I need is this HMM "architecture":
X1----X1----X1----...X1 (Markov Chain for input parameter 1 =>
monitoring X1's changes over time)
X2----X2----X2----...X2 (Markov Chain for intput parameter 2 =>
monitoring X2's changes over time)
Y-----Y-----Y-----...Y (Output value's changes over time)
I think this architecture would allow me to train and predict output Y
based on inputs X1 and X2.
3. Can we get output probabilities from the HMM for a concrete state?
Many thanks in advance!
Best regards,
Svetlomir.
