Array as time series?

A few indications about multivate analyses you can perform on time series:

- You can use any multivariate analysis like PCA and co, clustering methods
and so, considering time as just another variable. Although not implemented
in R, there are clustering methods with time connexity, i.e., in the final
dendrogram, all stations apppear in the chronological order and clusters are
constrained between adjacent stations in time (or space) only.

- A few multivariate analyses specific to space-time series:
+ Distogram

Mackas, D.L., 1984. Spatial autocorrelation of plankton community
composition in a continental shelf ecosystem. Limnol. Ecol., 20:451-471.

+ multiple autocorrelation with methods like crossD2 and D2 to the center.

Ibanez, F., 1981. Immediate detection of heterogeneities in continuous
multivariate oceanographic recordings. Application to time series analysis
of changes in the bay of Villefranche sur mer. Limnol. Oceanogr.,
26:336-349.

Ibanez, F., 1991. Treatment of the data deriving from the COST 647 project
on coastal benthic ecology: The within-site analysis. In: B. Keegan (ed),
Space and time series data analysis in coastal benthic ecology. Pp 5-43.

I certainly speak too much about something that is not finished yet... but
an R package for such multivariate time series analyses is in preparation
(due to the end of the year). See: http://www.sciviews.org/_passtec (in
French for the moment only).

All the best,

Philippe Grosjean


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Dear R-helpers,

I have 4-dimensional atmospheric data (x,y,z,t), which I want to analyse
on spatio-temporal diversities.
As far as I understand there only exists the possibility to construct
time series as two-dimensional matrices (mts).
For the moment, I hold it in different objects:

1. a four-dimensional array for the spatial related analyses
2. a two-dimensional mts timeserie, which was derived from 1. by
computing spatial means.

But, still this doesn't help for combined spatio-temporal analysis.

One could regard the time dimension as just another linearily spaced
dimension in the four-dimensional array, but when it comes analyses and
graphics output it gets complicated, since one can't use all timeseries
(ts-) related functions (require ts attributes).

Could you provide me some comments, workarounds or your experiences on
similar problems.

Thanks in advance,

Christian

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