Ultrametric wavelet regression of multivariate time series: Application to Colombian conflict analysis

Abstract

We first pursue the study of how hierarchy provides a well-adapted tool for the analysis of change. Then, using time sequence-constrained hierarchical clustering, we develop the practical aspects of a new approach to wavelet regression. This provides a new way to link hierarchical relationships in a multi-variate time-series data set with external signals. Violence data from the Colombian conflict in the years 1990-2004 are used throughout. We conclude with some proposals for further study on the relationship between social violence and market forces, viz., between the Colombian conflict and the U.S. narcotics market.