"Hans Werner Borchers" <[EMAIL PROTECTED]> wrote in message news:[EMAIL PROTECTED] > I've got some time series representing measurements from a physical > process, like atomic decay data. These time series look almost > random, but should hopefully be distinguishable as they were taken > under different conditions. > > I am looking for statistical approaches that are sensitive enough to > discriminate between such series of measurements. Preferably, there > are also implementations in R. > > Please note that I am not interested in tests of random number > generators, but on tests that can discriminate time series based on > statistical (or mining) features. Simple summary tests do not work, > also some of the simpler non-linear tests failed.
I've used Lomb-Scargle periodograms to look for periodic genes in fairly short time series from microarray experiments. A paper, "Significance testing of periodogram ordinates", Chris Koen, Astrophysical Journal, 348:700-702, 1990, makes the case that the Lomb-Scargle test really is: H1: the observations do not constitute noise. Koen's paper goes on to say a different statistical test should be used for periodicity. Perhaps the Lomb-Scargle test is still valid to discriminate noise. You might want to try it with your data. I've run a number of numerical experiments using Lomb-Scargle, and even when the p-values wouldn't keep a statistician happy, the Lomb-Scargle p-values "find" non-random periodic series fairly well. I've never made a package out of my Lomb-Scargle code, but perhaps the R code here will get you started: http://research.stowers-institute.org/efg/2005/LombScargle/R/index.htm Based on this paper: http://bioinformatics.oxfordjournals.org/cgi/content/abstract/bti789?ijkey=fD5aAeldrkzz765&keytype=ref efg Earl F. Glynn Scientific Programmer Stowers Institute for Medical Research ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
