Record Details


Field	Value

Title	A shape-based approach to conflict forecasting

Identifier	https://doi.org/10.7910/DVN/1BZD4Q

Creator	Thomas Chadefaux

Publisher	Harvard Dataverse

Description	Do conflict processes exhibit repeating patterns over time? And if so, can we exploit the recurring shapes and structures of the time series to forecast the evolution of conflict? Theory has long focused on the sequence of events that precedes conflicts (e.g., escalation or brinkmanship). Yet, current empirical research is unable to represent these complex interactions unfolding over time because it attempts to match cases on the raw value of covariates, and not on their structure or shape. As a result, it cannot easily represent real-world relations which may, for example, follow a long alternation of escalation and détente, in various orders and at various speeds. Here, I aim to address these issues using recent machine-learning methods derived from pattern recognition in time series to study the dynamics of casualties in civil war processes. I find that the methods perform well on out-of-sample forecasts, and in particular yield Mean Squared Errors that are lower than the competition benchmark. We discuss the implication for conflict research and the importance of comparing entire sequences rather than isolated observations in time.

Subject	Social Sciences Civil war Conflict Methodology forecasting dynamic time warping shape

Contributor	Journal, International Interactions

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