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