Least-squares spectral analysis
Periodicity computation method / From Wikipedia, the free encyclopedia
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Least-squares spectral analysis (LSSA) is a method of estimating a frequency spectrum based on a least-squares fit of sinusoids to data samples, similar to Fourier analysis.[1][2] Fourier analysis, the most used spectral method in science, generally boosts long-periodic noise in the long and gapped records; LSSA mitigates such problems.[3] Unlike in Fourier analysis, data need not be equally spaced to use LSSA.
Developed in 1969[4] and 1971,[5] LSSA is also known as the Vaníček method and the Gauss-Vaniček method after Petr Vaníček,[6][7] and as the Lomb method[3] or the Lomb–Scargle periodogram,[2][8] based on the simplifications first by Nicholas R. Lomb[9] and then by Jeffrey D. Scargle.[10]