Nested Sampling for ARIMA Model Selection in Astronomical Time-Series Analysis

Abstract: The upcoming era of large scale, high cadence astronomical surveys demands efficient and robust methods for time series analysis. ARIMA models provide a versatile parametric description of stochastic variability in this context. However, their practical use is limited by the challenge of selecting optimal model orders while avoiding overfitting. We present a novel solution to this problem using a Bayesian framework for time series modelling in astronomy by combining Autoregressive Integrated Moving Average (ARIMA) models with the Nested Sampling algorithm. Our method yields Bayesian evidences for model comparison and also incorporates an intrinsic Occam's penalty for unnecessary model complexity. A vectorized ARIMA Nested Sampling framework is implemented allowing us to perform model selection across grids of Autoregressive (AR) and Moving Average (MA) orders, with efficient inference of selected model parameters. The method is validated on simulated and real astronomical time series, including the yearly sunspots number record, Kepler Lightcurves data of the red giant KIC 12008916, and TESS photometry of the exoplanet host star Ross 176. In all cases, the algorithm correctly identified the true or best-fitting model while simultaneously yielding well constrained posterior distributions for the model parameters. Our results demonstrate that Nested Sampling offers a potentially rigorous alternative to autoregressive model selection in astronomical time series analysis.