Emulated FPE Order Identification for Autoregressive Models of Big Multivariate Time Series

Abstract {Simulation-based evaluation is used to demonstrate computational efficiency and predictive accuracy gains from emulator-based order selection}. Order identification using estimation method for large multivariate time series models presents substantial computational challenges, primarily at the estimation stage. Existing recommendations for model order selection in high-dimensional time series have largely focused on identifying models that best fit the observed data. When the goal of the analysis is prediction, alternative selection criteria may be more appropriate. This paper proposes an efficient, prediction-based approach to autoregressive model order identification for big multivariate time series. The simulation results demonstrate that the proposed method can significantly reduce computational time while still yielding accurate and reliable model orders for forecasting purposes. The method is illustrated with a big multivariate time series of weekly initial unemployment claims across 20 U.S. states.