On Feb 26, 2009, at 9:54 AM, (Ted Harding) wrote:
On 26-Feb-09 13:54:51, David Winsemius wrote:
I saw Gabor's reply but have a clarification to request. You say you
want to remove low frequency components but then you request
smoothing
functions. The term "smoothing" implies removal of high-frequency
components of a series.
If you produce a smoothed series, your result of course contains
the low-frequency comsponents, with the high-frequency components
removed.
But if you then subtract that from the original series, your result
contains the high-frequency components, with the low-frequency
compinents removed.
Yes. The time series term would be "detrending" or "de-trending".
Moving-average is one way of smoothing (but can introduce periodic
components which were not there to start with).
Filtering a time-series is a very open-ended activity! In many
cases a useful start is exploration of the spectral properties
of the series, for which R has several functions. 'spectrum()'
in the stats package (loaded bvy default) is one basic function.
help.search("time series") will throw up a lot of functions.
You might want to look at package 'ltsa' (linear time series
analysis).
Alternatively, if yuou already have good information about the
frequency-structure of the series, or (for instance) know that
it has a will-defined seasonal component, then you could embark
on designing a transfer function specifically tuned to the job.
Have a look at RSiteSearch("{transfer function}")
As the OP's reply indicates, she is already using wavelet analysis.
My question at this point is whether she should just be advised to
ignore
the low frequency components and concentrate on the middle and high
frequency components. If you already have some sort of spectral
decomposition, there should be no necessity of a subtraction or
de-trending step.
--
David Winsemius
Hoping this helps,
Ted.
If smoothing really is your goal then additional R resource would be
smooth.spline, loess (or lowess), ksmooth, or using smoothing terms
in
regressions. Venables and Ripley have quite a few worked examples of
such in MASS.
--
David Winsemius
On Feb 26, 2009, at 7:07 AM, <mau...@alice.it> wrote:
I am looking for some help at removing low-frequency components from
a signal, through Moving Average on a sliding window.
I understand thiis is a smoothing procedure that I never done in my
life before .. sigh.
I searched R archives and found "rollmean", "MovingAverages {TTR}",
"SymmetricMA".
None of the above mantioned functions seems to accept the smoothing
polynomial order and the sliding window with as input parameters.
Maybe I am missing something.
I wonder whether there is some building blocks in R if not even a
function which does it all (I do not expect that much,though).
Even some literature references and/or tutorials are very welcome.
Thank you so much,
Maura
tutti i telefonini TIM!
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E-Mail: (Ted Harding) <ted.hard...@manchester.ac.uk>
Fax-to-email: +44 (0)870 094 0861
Date: 26-Feb-09 Time: 14:54:43
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