Hi Peter, generally speaking, wavelets are known to be good at extracting signal from noisy data and are adaptive but I am not familiar with any R implementation of wavelets. A simple way of looking at changes would be to use CUSUM (strucchange package). I hope this helps. Ansel.
On 1/30/07, Peter Nimda <[EMAIL PROTECTED]> wrote: > > Hallo, > > my noisy time series represent a fading signal comprising of long > enough parts with a simple trend inside of each such a part. > Transition from one part into another is always a non-smooth > and very sharp/acute. In other words I have a piecewise > polynomial noisy curve asymptotically converging to the > biased constant, points between pieces are non-differentiable. > > I am looking for implementations of models adequate for such a > data. Are there any possibilities to adapt the ARIMA or > MCMC? > > Many thanks in advance for any help/URLs > > ______________________________________________ > [email protected] 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. > [[alternative HTML version deleted]] ______________________________________________ [email protected] 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.
