Moving Average
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, <mauede at 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!
>>>
>>>
>>> [[alternative HTML version deleted]]
>>>
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>>> and provide commented, minimal, self-contained, reproducible code.
>>
>> ______________________________________________
>> R-help at 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.
>
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> E-Mail: (Ted Harding) <Ted.Harding at manchester.ac.uk>
> Fax-to-email: +44 (0)870 094 0861
> Date: 26-Feb-09 Time: 14:54:43
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