Mathieu,
Thanks for your email.
Problem 1:
* I will have a look to the grid search model.
* If you have any example of testing hmm using the grid search
module, it will be brilliant. (basic code are more than welcome !!!)
Problem 2 ( classification)
It's not clear for me (I will work first on the problem 1).
Regards and merci encore.
Didier
Didier Vila, PhD | Risk | CapQuest Group Ltd | Fleet 27 | Rye Close |
Fleet | Hampshire | GU51 2QQ | Fax: 0871 574 2992 | Email:
dv...@capquestco.com <mailto:mbruna...@capquestco.com>
From: mblon...@gmail.com [mailto:mblon...@gmail.com] On Behalf Of
Mathieu Blondel
Sent: 18 October 2012 06:00
To: Didier Vila
Cc: scikit-learn-general@lists.sourceforge.net
Subject: Re: [Scikit-learn-general] HMM: Determination of the state
numbers
On Thu, Oct 18, 2012 at 1:37 AM, Didier Vila <dv...@capquestco.com>
wrote:
Problem 1: My problem is a risk problem at the moment.
* I want to represent the behaviour of my 20 000 time series and
generates some monte carlo simulations using the method.sample()
* Intuitively, i have to choose between 1 or 3 ( max 4) the
number of states.
* Then, I want to capture the risk for each time series and the
risk at the aggregate level.( I will generate 100 Monte Carlo
Simulations)
Alright, then optimizing "score" may make sense it that case.
* Question 1: I don t see how I can train and cross
validate my HMM in Scikit Learn ( First time I use Scikit Learn for
this purpose)
* Question 2: the lenght of the time serie is 32 periods,
is it enough to make cross validation and validation ?
Have a look at http://scikit-learn.org/0.12/modules/grid_search.html.
Problem 2: Classification
* In the near future, I will try to make some
classification of time series but I have no ideas how to handle the
problem ? Should I use an SVM ? Can you refer any paper ?
You can group your time series per class and train one HMM per class
with those time series. Then given a new time series, you can decide its
class by the argmax of the Bayes rule:
Class = argmax P(Class | Time Series) = argmax P(Time Series | Class) *
P(Class) / P(Time Series) = argmax P(Time Series | Class) * P(Class)
P(Time Series | Class) can be computed by the forward algorithm or can
be approximated by the Viterbi algorithm (which is more numerically
stable).
P(Class) can be computed by counting the number of time series in each
class.
Generic Questions: I was wondering if your algorithm is
developed iin Python ? Do you think your algo is relevant to apply to my
business problem ?
My method is useful for classifying time series which are made of
smaller parts whose label you don't know. So I don't think it would work
for you.
Mathieu
This e-mail is intended solely for the addressee, is strictly confidential and
may also be legally privileged. If you are not the addressee please do not
read, print, re-transmit, store or act in reliance on it or any attachments.
Instead, please email it back to the sender and then immediately permanently
delete it. E-mail communications cannot be guaranteed to be secure or error
free, as information could be intercepted, corrupted, amended, lost, destroyed,
arrive late or incomplete, or contain viruses. We do not accept liability for
any such matters or their consequences. Anyone who communicates with us by
e-mail is taken to accept the risks in doing so. Opinions, conclusions and
other information in this e-mail and any attachments are solely those of the
author and do not represent those of CapQuest Group Limited or any of its
subsidiaries unless otherwise stated. CapQuest Group Limited (registered number
4936030), CapQuest Debt Recovery Limited (registered number 3772278), CapQuest
Investments Limited (registered number 5245825), CapQuest Asset Management
Limited (registered number 5245829) and CapQuest Mortgage Servicing Limited
(registered number 05821008) are all limited companies registered in England
and Wales with their registered offices at Fleet 27, Rye Close, Fleet,
Hampshire, GU51 2QQ. Each company is a separate and independent legal entity.
None of the companies have any liability for each other's acts or omissions.
This communication is from the company named in the sender's details above.------------------------------------------------------------------------------
Everyone hates slow websites. So do we.
Make your web apps faster with AppDynamics
Download AppDynamics Lite for free today:
http://p.sf.net/sfu/appdyn_sfd2d_oct
_______________________________________________
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
