I have an irregular time series in a Zoo object, and I've been unable to
find any way to do an FFT on it. More precisely, I'd like to do an NFFT
(non-equispaced / non-uniform time FFT) on the data.
The data is timestamped samples from a cheap self-logging
accelerometer. The data is weakly regular, with the following
characteristics:
- short gaps every ~20ms
- large gaps every ~200ms
- jitter/noise in the timestamp
The gaps cover ~10% of the acquisition time. And they occur often
enough that the uninterrupted portions of the data are too short to
yield useful individual FFT results, even without timestamp noise.
My searches have revealed no NFFT support in R, but I'm hoping it may be
known under some other name (just as non-uniform time series are known
as 'zoo' rather than 'nts' or 'nuts').
I'm using R through RPy, so any solution that makes use of numpy/scipy
would also work. And I care more about accuracy than speed, so a
non-library solution in R or Python would also work.
Alternatively, is there a technique by which multiple FFTs over smaller
(incomplete) data regions may be combined to yield an improved view of
the whole? My experiments have so far yielded only useless results, but
I'm getting ready to try PCA across the set of partial FFTs.
TIA,
-BobC
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