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From Svetlomir Dimitrov Kasabov <>
Subject Re: Logistic Regression + Time Series
Date Mon, 06 Jun 2011 13:46:42 GMT
Thanks for the useful replies, I really appreciate that!

@Ted and Hector: My initial parameters (predictors) are blood  
pressures, heart rates, etc: they come every minute from a patient's  
In my implementation, I plan refering to this Paper : on page 7 (Table 1) you  
can see the parameters used.  On page 17, figure 4 you can see  
vizualization of the prediction using time series:

I think I still plan using the logistic regression implementation  
(since I am already worked into it), but I am confuzed how to  
implement time series with Mahout. Should I create periodically (for  
example every 15 minutes) a new logistic regression model, in order to  
predict the probability of instability? Then the amount of training  
data depends on the 'time window for the past' that I will be using.   
For example, for data only two hours from the past, I will have only  
circa 60 * 2 = 120 examples for creating a temporal model (I assume  
that I will need one compound data vector pro minute, including blood  
pressures, heart rates, etc...)

Or should I implement the time series so, that I train the model only  
once with old data of many patients and the training algorithm will be  
so, that it checks what is the patient's hemodynamic stability in two  
hours (since this information is also known during the training)? In  
this case, I will potentually have many more examples (one million or  

Many thanks, best regards and sorry for the long post.


Zitat von Ted Dunning <>:

> 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 <> wrote:
>> You can also try HMMs:
>> 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 <
>>> 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
>> (tech + travel)
>> (book reviews)

Svetlomir Dimitrov Kasabov

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