Abstract

In this talk we consider analysis problems for time series that are observed at nodes of a large network structure. Such problems commonly appear in a vast array of fields, such as environmental time series observed at different spatial locations or measurements from computer system monitoring. The time series observed on the network might exhibit different characteristics such as nonstationary behaviour or strong correlation, and the nodal series evolve according to the inherent spatial structure.

The new methodology we develop hinges on reducing dimensionality of the original data through a change of basis. The basis we propose is a second generation wavelet basis which operates on spatial structures. As such, the (large) observed data is replaced by data over a reduced network topology. We give examples of the potential of this dimension reduction method for time series analysis tasks. This is joint work with Marina Knight (University of York) and Guy Nason (University of Bristol).