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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