Greetings all,
I am using Fourier analysis to search for periodicities in IP network traffic
by generating periodograms and then visually examining them for large, distinct
peaks.
However, in many cases it is not readily apparent where there are
periodicities. I have no experience with discrete maths so I've come up
against a block here: How do I define what the "noise floor" is and what peaks
rising above it are significant enough to warrant further investigation?
I had thought to try to detect peaks as outliers by using confidence intervals
(assuming that the "Power" vector was normally distributed) but I'm not sure if
this is statistically valid. If anyone can provide help, or point me to some
resources on the subject, then I'd appreciate it.
Incidentally, I have tried to use other methods (the Lomb-Scargle method in
particular) but haven't found any especially well-suited to the problem.
Best regards,
Pete
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