LagFormer: regressing periods from sparse, irregularly sampled series

Period estimation for irregularly sampled series is usually a search over trial frequencies, on a grid whose density grows with record length and whose highest peak can be a harmonic of the true period. Several learned estimators take such frequency-resolved inputs and inherit the same grid. We introduce LagFormer, which identifies the period from self-similarity on a logarithmic lag axis, where a fixed relative resolution suffices. An exact correlogram bins every pair of observations by time difference, handling arbitrary sampling without interpolation, and compares observed values alongside channels learned by a transformer. Since each period bin spans a whole number of lag bins, every candidate period’s multiples lie at fixed lag offsets, which a harmonic comb gathers for a network outputting a distribution over log-period. A local refinement sharpens the estimate within the selected harmonic. Trained only on synthetic series that replay real sampling and noise, LagFormer finds the period within 10% on 90.2% of held-out synthetic series, against 69.3% for a Lomb–Scargle periodogram and 85.0% for PDM, without a fine global frequency search. On real light curves it agrees with the catalogue period on 80.6%, and its refined period folds the data more tightly than the catalogue period on 52.8% of a common same-harmonic subset, nearly the 53.7% from refining the catalogue period itself.