Modern Time-Series and Spectral Methods for Analyzing Solar and Stellar Oscillatory Signals

Abstract

Time-series analysis plays a central role in understanding oscillatory and wave phenomena in solar and stellar atmospheres. However, astrophysical observations are inherently affected by instrumental noise, non-stationary dynamics, and uneven sampling. This review provides a comprehensive overview and comparative analysis of principal methods for detecting and characterizing periodicities in solar and stellar signals. We cover Fourier-transform-based transforms, nonlinear-fitting-based methods (Lomb--Scargle periodogram), time-frequency methods (wavelet and synchrosqueezed transforms), and adaptive decomposition techniques (Empirical Mode Decomposition). Advanced statistical significance tests, including false-alarm probability, autoregressive models, and Bayesian Markov Chain Monte Carlo (MCMC) approaches, are discussed their practical limitations and misuse risks. Through comparative analysis using synthetic benchmarks, we provide guidelines for selecting methods based on signal stationarity, sampling regularity, and noise characteristics. Finally, we outline future directions that integrate Bayesian inference with time-frequency analysis to achieve both statistical rigor and temporal localization in studying non-stationary solar and stellar oscillations.