[R] Classification of Multivariate Time Series

Roy Mendelssohn - NOAA Federal roy.mendelssohn at noaa.gov
Mon May 27 15:39:37 CEST 2013

Look at:

State - Space Discrimination and Clustering of. Atmospheric Time Series Data. Based on Kullback Information Measures. Thomas Bengtsson

If you Google the topic, there are  host of other papers too, but the one meshes with exiting star-space methods.


On May 27, 2013, at 4:34 AM, Lorenzo Isella <lorenzo.isella at gmail.com> wrote:

> Dear All,
> Apologies for not posting a code snippet, but I really need a pointer about
> a methodology to look at my data and possibly some R package which can ease
> my task.
> I am given a set consisting of several multivariate noisy time series,
> let's call it {A}.
> Each A_i in {A}, in turn, consists of several numerical time series.
> Then I have another set of shorter time series {B}.
> Now, for every B_j in {B}, I need to determine the time series A_i where
> most likely B_j comes from (A_i is not just a subset of B_j).
> In other words, I need to determine the distance between A_i and B_j.
> I was thinking about the Mahalanobis distance described here.
> http://en.wikipedia.org/wiki/Mahalanobis_distance
> However, I have several questions in my head
> 1) With the Mahalanobis distance, do I lose the info about the time
> structure of the data? I am not just comparing some distributions, but some
> time series and the ordering of the data is important.
> 2) Even if the use of the Mahalanobis distance was appropriate, it involves
> the calculation of a covariance matrix and a mean.
> Should I average A_i or B_j (or a subset of B_j having the same length as
> A_i)? And should I use a correlation matrix based on A_i or B_j?
> Any suggestion is welcome.
> Lorenzo
> 	[[alternative HTML version deleted]]
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Roy Mendelssohn
Supervisory Operations Research Analyst
Environmental Research Division
Southwest Fisheries Science Center
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