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 <p.nimda@gmail.com> 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
>
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