search for: 365readings

...you want to find the spectral density aka what frequencies explain most of the variance then I would suggest the spectral density. This can be implemented with spec.pgram(). This is conducted with the fast fourier transform algorithm. a<-ts(data, frequency = 1) #make the time series with 365readings/365days ?spec.pgram and you should be able to take it from here This will give you the raw periodogram and the dominant frequencies after you smooth the periodogram. If your intention is to just fit a curve to your data there are many types of cuve fitting options moving average etc....