> 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 <

[hidden email]>

> 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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