Ultrametric Wavelet Regression of Multivariate Time Series: Application to Colombian Conflict Analysis

TL;DR

This paper introduces a novel wavelet regression method based on ultrametric hierarchical clustering to analyze multivariate time series, exemplified by Colombian conflict data, revealing links between violence and external factors.

Contribution

It develops a new wavelet regression approach that incorporates hierarchical relationships in multivariate time series data, applied to conflict analysis.

Findings

Hierarchical structure effectively captures change in conflict data

Links identified between violence patterns and external signals

Method demonstrates potential for social conflict analysis

Abstract

We first pursue the study of how hierarchy provides a well-adapted tool for the analysis of change. Then, using a 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 multivariate time series data set with external signals. Violence data from the Colombian conflict in the years 1990 to 2004 is 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 US narcotics market.