You might also like to have a look at use of random matrix theory:

http://arxiv.org/pdf/cond-mat/0508122.pdf
https://www.math.nyu.edu/faculty/avellane/LalouxPCA.pdf

Regards
Anmol Sethy

On Thu, Feb 23, 2012 at 6:17 PM, Vincent Zoonekynd <zoonek@gmail.com> wrote:

> Here are a few ideas to cluster time series,
> with more references.
>
> 1. Build the minimum spanning tree on the correlation
> matrix. The result is usually very noisy: you may
> want to resample the data to see how the trees
> change.
> This usually gives acceptable results: for
> instance, you can often recognise industry groups
> from daily or weekly stock returns.
> A few references:
>  An introduction to econophysics, Correlations and complexity in
> finance, R.N. Mantegna and H.E. Stanley (2000)
>  http://arxiv.org/abs/cond-mat/0302546
>  http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1617257
>  http://arxiv.org/abs/0806.4714
>  http://arxiv.org/abs/0708.0562
>  http://arxiv.org/abs/cond-mat/0412411
>
> 2. Threshold the correlation matrix and consider the
> result as the incidence matrix of a graph: its
> connected components can be interpreted as
> clusters.
>
> 3. Convert the correlation matrix to a distance
> matrix, and apply the standard clustering
> algorithms: k-means, hierarchical clustering,
> Kohonen networks, etc.  You may want to try these
> with various estimators of the correlation matrix:
> for instance, shrinkage estimators should help
> reduce the noise in the data.
>
> 4. If you accept methods not based on correlation,
> you can model your times series, e.g., with
> econometric models (ARMA, GARCH, etc.), stochastic
> differential equations (the "Markov operator
> distance" at the end of "Option pricing and
> estimation of financial models with R", by
> S.M. Iacus), wavelet decomposition, iSAX
> (http://www.cs.ucr.edu/~eamonn/iSAX/iSAX.html),
> etc., and cluster the coefficients of those
> models.
>
> -- Vincent
>
> On 23 February 2012 18:29, julien cuisinier <j_cuisinier@hotmail.com>
> wrote:
> >
> > Hi Michael,
> >
> >
> >
> > A very general question here with little input from you...I am not
> surprised to see little feedback
> >
> > I have been looking for something similar & same result so I do not
> think it exist yet. I am a complete newbie in clustering but looking around
> there are plenty of R function available, nothing that I could find as
> simple as using correlation per se.
> >
> > Thinking about it Im not sure how it would work & anything I can think
> of would be quite sensitive to the starting point (e.g. calculate pair-wise
> correls within a market, then start by one stock & cluster with it all
> other stocks with corrells higher than a certain threshold?) May be some
> recursive function trying many different starting points? But then what to
> do with the resulting different cluster structure?
> >
> > Could you share with the list what reference (not in R) you found on the
> topic? That would be great if you could share / bring something to the list
> as well & then see if we can build that in? (very very ambitious of me here
> =)
> >
> >
> >
> > Thanks & regards,
> > Julien
>
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