Emulated order identification for models of big time series data

This interdisciplinary research includes elements of computing, optimization, and statistics for big data Specifically, it addresses model order identification aspects of big time series data Computing and minimizing information criteria, such as BIC, on a grid of integer orders becomes prohibitive for time series recorded at a large number of time points We propose to compute information criteria only for a sample of integer orders and use kriging‐based methods to emulate the information criteria on the rest of the grid Then we use an efficient global optimization (EGO) algorithm to identify the orders The method is applied to both ARMA and ARMA‐GARCH models We simulated times series from each type of model of prespecified orders and applied the method to identify the orders We also used real big time series with tens of thousands of time points to illustrate the method In particular, we used sentiment scores for news headlines on the economy for ARMA models, and the NASDAQ daily returns for ARMA‐GARCH models, from the beginning in 1971 to mid‐April 2020 in the early stages of the COVID‐19 pandemic The proposed method identifies efficiently and accurately the orders of models for big time series data [ABSTRACT FROM AUTHOR] Copyright of Statistical Analysis & Data Mining is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )