Autoregressive times series methods for time domain astronomy

Eric D. Feigelson, G. Jogesh Babu, Gabriel A. Caceres

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


Celestial objects exhibit a wide range of variability in brightness at different wavebands. Surprisingly, the most common methods for characterizing time series in statistics-parametric autoregressive modeling-are rarely used to interpret astronomical light curves. We review standard ARMA, ARIMA, and ARFIMA (autoregressive moving average fractionally integrated) models that treat short-memory autocorrelation, long-memory 1/fα "red noise," and nonstationary trends. Though designed for evenly spaced time series, moderately irregular cadences can be treated as evenly-spaced time series with missing data. Fitting algorithms are efficient and software implementations are widely available. We apply ARIMA models to light curves of four variable stars, discussing their effectiveness for different temporal characteristics. A variety of extensions to ARIMA are outlined, with emphasis on recently developed continuous-time models like CARMA and CARFIMA designed for irregularly spaced time series. Strengths and weakness of ARIMA-type modeling for astronomical data analysis and astrophysical insights are reviewed.

Original languageEnglish (US)
Article number80
JournalFrontiers in Physics
Issue numberAUG
StatePublished - Aug 7 2018

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Materials Science (miscellaneous)
  • Mathematical Physics
  • General Physics and Astronomy
  • Physical and Theoretical Chemistry


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