Code and symbolic scripts for double (two-frequency) Lomb–Scargle periodograms and omnigrams, based on Scargle & Wagner’s astronomical time-series analysis work.
End-to-End Python implementation of LPPLS (Log-Periodic Power Law Singularity) framework for detecting financial bubbles and critical transitions. Features Filimonov-Sornette calibration, Lagrange regularization, Lomb-Scargle spectral validation, and Monte Carlo significance testing. Complete computational replication of Hosseinzadeh (2025).
Physics-aware ML analysis of 3I/ATLAS (photometry + spectroscopy).
Lomb Scargle L1 version, as described in Makarov et al. 2024 (arXiv:2405.12324)
Published large-scale irregular time series regression pipeline (arXiv:2311.04470) integrating Lomb–Scargle period detection, feature engineering, and validated statistical modeling.
Bayesian Generalized Lomb-Scargle (BGLS) and Stacked BGLS periodograms for Julia. Implements Mortier et al. (2015) and Mortier & Collier Cameron (2017).
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