Forecasting violent events in the Middle East and North Africa using the Hidden Markov Model and regularized autoregressive models

Abstract

This paper focuses on forecasting Military Action-type events by both state and non-state actors. Here we demonstrate that the dynamics of these types of events can be adequately described by a Hidden Markov Model (HMM) where the hidden states correspond to different operational regimes of an actor, and observations correspond to event frequency—and the HMM effectively predicts events with different lead times. We also demonstrate that one can enrich statistical time series-based methods that work only on historical data by exploiting predictive signals in real-time external data streams. We demonstrate the superior predictive power of the proposed models with evaluation of recent data capturing activities over two groups, ISIS and the Syrian Arab Military, two countries, Syria and Iraq, and two cities, Aleppo and Mosul. We also present an approach to converting predictions of the proposed models to real-world warnings.