Abstract
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 language | English (US) |
|---|---|
| Article number | 80 |
| Journal | Frontiers in Physics |
| Volume | 6 |
| Issue number | AUG |
| DOIs | |
| State | Published - Aug 7 2018 |
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All Science Journal Classification (ASJC) codes
- Biophysics
- Materials Science (miscellaneous)
- Mathematical Physics
- Physics and Astronomy(all)
- Physical and Theoretical Chemistry
Cite this
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Autoregressive times series methods for time domain astronomy. / Feigelson, Eric; Babu, G. Jogesh; Caceres, Gabriel A.
In: Frontiers in Physics, Vol. 6, No. AUG, 80, 07.08.2018.Research output: Contribution to journal › Article
TY - JOUR
T1 - Autoregressive times series methods for time domain astronomy
AU - Feigelson, Eric
AU - Babu, G. Jogesh
AU - Caceres, Gabriel A.
PY - 2018/8/7
Y1 - 2018/8/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85052871688&partnerID=8YFLogxK
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U2 - 10.3389/fphy.2018.00080
DO - 10.3389/fphy.2018.00080
M3 - Article
VL - 6
JO - Frontiers in Physics
JF - Frontiers in Physics
SN - 2296-424X
IS - AUG
M1 - 80
ER -