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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Logistic Regression + Time Series
Date Mon, 06 Jun 2011 10:12:00 GMT
What Hector said.

You will need to extract features from your time history.

The question also comes up about how large is  your data set.  If it is less
than 100,000 training examples or so, then you will probably be better off
using a system like R which handles that much data easily and has
essentially every kind of classifier available for you to try.

If you have 1 million training examples or more, then Mahout begins to
dominate alternatives.  Even there, Mahout is currently optimized for sparse
data which is not what you have.  My guess is that using the
OnlineLogisticRegression or some of Hector's recent patches is the way to
go. The AdaptiveLogisticRegression is heavily oriented around per term
annealing and magic knob tuning in the context of sparse data.

Can you post your data?

On Sun, Jun 5, 2011 at 10:04 AM, Hector Yee <hector.yee@gmail.com> wrote:

> You can also try HMMs:
>
>
> https://builds.apache.org/job/Mahout-Quality/javadoc/org/apache/mahout/classifier/sequencelearning/hmm/package-tree.html
>
> If you want to do it with a classifier you can window your time series and
> make a training set
>
> e.g.
>
> label, feature
> stable, (last X seconds of time series)
> unstable, (last X seconds of time series)
>
> On Sun, Jun 5, 2011 at 8:08 AM, Svetlomir Dimitrov Kasabov <
> svetlomir.kasabov@smail.inf.fh-bonn-rhein-sieg.de> wrote:
>
> > Hello,
> >
> > I plan using Apache Mahout's Logistic Regression (LR) implementation in
> my
> > Master-Thesis. We plan using time series in order to predict, whether a
> > particular patient will have an instable blood flow soon or not. Thats's
> why
> > I want to ask you if it is possible to use Mahout in connection with time
> > series ? Do you see any potential problems / risks ?
> >
> > Many thanks and best regards!
> >
> > Svetlomir Kasabov.
> >
> >
> >
> > --
> > Svetlomir Dimitrov Kasabov
> >
> > ----------------------------------------------------------------
> > This message was sent using IMP, the Internet Messaging Program.
> >
> >
>
>
> --
> Yee Yang Li Hector
> http://hectorgon.blogspot.com/ (tech + travel)
> http://hectorgon.com (book reviews)
>

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