Model selection for big multivariate time series data using emulators

Order identification for models of big time series data presents computational challenges. Results from previous studies on big univariate time series suggest that methods based on kriging and optimization can reduce the computing time substantially while providing adequately plausible model orders. In today’s world, however, one must analyze multiple big time series simultaneously, such as multiple stocks or measuring humidity in various rooms of a house. This becomes a much bigger computational challenge to address, as one must take into account the cross-correlation between the individual time series. The goal of this paper is to detail a method to fit big multivariate time series. The results show that the proposed technique can substantially decrease computing time while still providing reasonably accurate model orders.