Novel bivariate autoregressive model for predicting and forecasting irregularly observed time series
arXiv:2104.12248 · doi:10.1093/mnras/stab1216
Abstract
In several disciplines it is common to find time series measured at irregular observational times. In particular, in astronomy there are a large number of surveys that gather information over irregular time gaps and in more than one passband. Some examples are Pan-STARRS, ZTF and also the LSST. However, current commonly used time series models that estimate the time dependency in astronomical light curves consider the information of each band separately (e.g, CIAR, IAR and CARMA models) disregarding the dependency that might exist between different passbands. In this paper we propose a novel bivariate model for irregularly sampled time series, called the bivariate irregular autoregressive (BIAR) model. The BIAR model assumes an autoregressive structure on each time series, it is stationary, and it allows to estimate the autocorrelation, the cross-correlation and the contemporary correlation between two unequally spaced time series. We implemented the BIAR model on light curves, in the g and r bands, obtained from the ZTF alerts processed by the ALeRCE broker. We show that if the light curves of the two bands are highly correlated, the model has more accurate forecast and prediction using the bivariate model than a similar method that uses only univariate information. Further, the estimated parameters of the BIAR are useful to characterize LongPeriod Variable Stars and to distinguish between classes of stochastic objects, providing promising features that can be used for classification purposes
14 pages, 7 figures, 6 tables, 2 apendices. Accepted for publication in the Monthly Notices of the Royal Astronomical Society (MNRAS)
References in corpus (21)
- Scikit-learn: Machine Learning in Python
- The Sloan Digital Sky Survey: Technical Summary
- LSST: from Science Drivers to Reference Design and Anticipated Data Products
- The Zwicky Transient Facility: System Overview, Performance, and First Results
- On characterising the variability properties of X-ray light curves from active galaxies
- On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data
- Flexible and Scalable Methods for Quantifying Stochastic Variability in the Era of Massive Time-Domain Astronomical Data Sets
- Periodograms for Multiband Astronomical Time Series
- Automated supervised classification of variable stars I. Methodology
- Correlated Multi-Waveband Variability in the Blazar 3C~279 from 1996 to 2007
- Alert Classification for the ALeRCE Broker System: The Light Curve Classifier
- Extracting Information from AGN Variability
- A machine learned classifier for RR Lyrae in the VVV survey
- Robust period estimation using mutual information for multi-band light curves in the synoptic survey era
- FATS: Feature Analysis for Time Series
- An irregular discrete time series model to identify residuals with autocorrelation in astronomical light curves
- A Hybrid Algorithm for Period Analysis from Multi-band Data with Sparse and Irregular Sampling for Arbitrary Light Curve Shapes
- Modeling Stochastic Variability in Multi-Band Time Series Data
- Discrete-time autoregressive model for unequally spaced time-series observations
- A Multiband Generalization of the Analysis of Variance Period Estimation Algorithm and the Effect of Inter-band Observing Cadence on Period Recovery Rate
- ELT Imaging of MWC 297 from the 23-m LBTI: Complex Disk Structure and a Companion Candidate