Did you have a look at Dynamic Time Warping and dtw package?
Best, E.
On Mon, May 27, 2013 at 01:34:42PM +0200, Lorenzo Isella 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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