Hello,
I have a very large data set (collected automatically) of animal heart
frequencies. The data of course forms a time series. My problem is I do
not only have the real heart frequencies, but also quite a lot of
non-sense noise which needs to be filtered out.
Visually inspecting the data (plotting data index against noisy heart
frequencies) one can see a band of denser data points, which make up the
true heart frequency, and the rest both above and below (that is, higher
and lower frequency) is the noise. My task is to filter out the noise,
that is the dense band.
As I have only limited knowledge of time-series analyses, and virtually
none of filtering techniques my first question is if there is a specific
statistical method you would suggest. My second question is if there is
a ready implementation in R that I can use. Finally, if you know of a
good book suitable for the problem so I can read more about the details
that would also be great.
Many thanks,
Thomas
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