Discovering optimally representative dynamical locations (ORDL) in big multivariate spatiotemporal data: A case study of precipitation in Australia from space to ground sensors

Abstract

We develop a method for discovering a set of optimally representative dynamical locations (ORDL), a small subset of observed locations that are the most informative of the dynamics of a real complex system, as embodied in big spatiotemporal data. We achieve this through a two-pronged approach: (a) by reducing the multivariate time series data into a small set of time series with minimal loss of information on the dynamics of the system, (b) by exploiting the best that remote sensing and in-situ observations can offer. In the former, we extend the recently proposed empirical dynamical quantiles for univariate time series to multivariate data using a directional statistical depth measure and p..