Spectral Subsampling MCMC for Stationary Multivariate Time Series
Date: 19 August 2021, Thursday
Time: 12pm AEST
Contact the organizer: Andriy Olenko a.olenko@latrobe.edu.au
Speaker: Dr Matias Quiroz, University of Technology Sydney
Abstract:
Spectral
subsampling MCMC was recently proposed to speed up Markov chain Monte
Carlo (MCMC) for long stationary univariate time series by subsampling
periodogram observations in the frequency domain. This talk presents an
extension of the approach to stationary multivariate time series. We
also propose a multivariate generalisation of the autoregressive
tempered fractionally differentiated moving average model (ARTFIMA). The
new model is shown to provide a better fit compared to multivariate
autoregressive moving average models for three real world examples. We
demonstrate that spectral subsampling may provide up to two orders of
magnitude faster estimation, while retaining MCMC sampling efficiency
and accuracy, compared to spectral methods using the full dataset.
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