Nested Sampling Algorithm Improves ARIMA Model Selection for Astronomical Time-Series Data
Astronomers have developed a GPU-accelerated framework that pairs ARIMA statistical models with the Nested Sampling algorithm to improve the selection and fitting of models for astronomical time-series data. The method was validated on simulated data and applied to real datasets including sunspot records, Kepler stellar light curves, and TESS quasar light curves. The approach offers a rigorous, computationally efficient alternative to traditional model selection techniques for the growing volume of data produced by large-scale sky surveys.
A team of researchers has introduced a novel framework combining Autoregressive Integrated Moving Average (ARIMA) models with the Nested Sampling algorithm to address the challenge of model order selection in astronomical time-series analysis. Implemented using JAX and Blackjax with GPU-acceleration support, the method computes Bayesian evidences for model comparison and applies an intrinsic Occam's penalty to guard against overfitting. The framework was first validated on simulated time series with known parameters, demonstrating accurate recovery of both model orders and parameter values. It was then applied to several real astronomical datasets: the historical sunspot number record, stellar light curves of KIC 12008916 and Kepler 17 from the Kepler mission, and quasar light curves of 3C 273 and S4 0954+65 from the TESS mission. The method successfully modeled stochastic variability and produced accurate multi-step forecasts in all cases except Kepler 17, where ARIMA models proved insufficient. The authors argue this approach is well-suited to the demands of modern high-cadence surveys that generate large volumes of time-series data. The paper, spanning 20 pages and 31 figures, has been submitted to Monthly Notices of the Royal Astronomical Society (MNRAS).
What's missing
The paper does not detail why ARIMA models failed for Kepler 17 specifically, nor what alternative modeling approaches might be more appropriate for that case. Computational benchmarks comparing runtime and resource costs against conventional model selection methods (e.g., AIC/BIC-based grid search) are not described in the abstract.
What different sources said
- arXiv astro-phCenter
Nested Sampling for ARIMA Model Selection in Astronomical Time-Series Analysis
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