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Computational study demonstrates an order N log N Fast Fourier Transform algorithm for Lomb-Scargle periodograms, highlighting drastic speedups for unevenly sampled time-series data.
The Lomb-Scargle method performs spectral analysis on unevenly sampled data and is known to be a powerful way to find, and test the significance of, weak periodic signals. The method has previously been thought to be 'slow', requiring of order 10(2)N(2) operations to analyze N data points. We show that Fast Fourier Transforms (FFTs) can be used in a novel way to make the computation of order 10(2)N log N. Despite its use of the FFT, the algorithm is in no way equivalent to conventional FFT periodogram analysis.
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Press et al. (1989) studied this question.
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