Regularly varying time series in Banach spaces

TL;DR

This paper develops a theoretical framework for analyzing extremal behavior in Banach space-valued time series, using regular variation and tail processes to understand joint extremes and dependence structures.

Contribution

It introduces a new equivalence for regular variation in Banach space-valued time series and characterizes the tail process, enabling advanced extremal analysis in spatial-temporal data.

Findings

Equivalence between regular variation and convergence of rescaled processes.

Characterization of tail and spectral processes for Banach space-valued series.

Application to linear processes with regularly varying distributions.

Abstract

When a spatial process is recorded over time and the observation at a given time instant is viewed as a point in a function space, the result is a time series taking values in a Banach space. To study the spatio-temporal extremal dynamics of such a time series, the latter is assumed to be jointly regularly varying. This assumption is shown to be equivalent to convergence in distribution of the rescaled time series conditionally on the event that at a given moment in time it is far away from the origin. The limit is called the tail process or the spectral process depending on the way of rescaling. These processes provide convenient starting points to study, for instance, joint survival functions, tail dependence coefficients, extremograms, extremal indices, and point processes of extremes. The theory applies to linear processes composed of infinite sums of linearly transformed independent random elements whose common distribution is regularly varying.