AdaptSPEC-X: Covariate-Dependent Spectral Modeling of Multiple Nonstationary Time Series. Issue 2 (3rd April 2022)

• Record Type: Journal Article
• Authors: Bertolacci, Michael; Rosen, Ori; Cripps, Edward; Cripps, Sally
• Is Part Of: Journal of computational and graphical statistics. Volume 31:Issue 2(2022)
• Page Start: 436
• Page End: 454
• Publication Date: 2022-04-03
• Subjects: Locally stationary time series -- Measles -- Multiple time series -- Rainfall -- Reversible jump Markov chain Monte Carlo -- Whittle likelihood; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2021.2000870
• Languages: English
• ISSNs: 1061-8600

Abstract

Abstract: We present the AdaptSPEC-X method for the joint analysis of a panel of possibly nonstationary time series. The approach is Bayesian and uses a covariate-dependent infinite mixture model to incorporate multiple time series, with mixture components parameterized by a time-varying mean and log spectrum. The mixture components are based on AdaptSPEC, a nonparametric model which adaptively divides the time series into an unknown number of segments and estimates the local log spectra by smoothing splines. AdaptSPEC-X extends AdaptSPEC in three ways. First, through the infinite mixture, it applies to multiple time series linked by covariates. Second, it can handle missing values, a common feature of time series which can cause difficulties for nonparametric spectral methods. Third, it allows for a time-varying mean. Through these extensions, AdaptSPEC-X can estimate time-varying means and spectra at observed and unobserved covariate values, allowing for predictive inference. Estimation is performed by Markov chain Monte Carlo (MCMC) methods, combining data augmentation, reversible jump, and Riemann manifold Hamiltonian Monte Carlo techniques. We evaluate the methodology using simulated data, and describe applications to Australian rainfall data and measles incidence in the United States. Software implementing the method proposed in this article is available in the R package BayesSpec. Supplementary files for this article are available online.