# Conflict Forecasting via Conformal Prediction for Markov Processes

arXiv: https://arxiv.org/abs/2604.25139 · HTML: https://mlsanity.com/paper/2604.25139/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [Adaptive Conformal Inference Under Distribution Shift](https://mlsanity.com/paper/2106.00170/) (2021)
- [Adaptive Conformal Predictions for Time Series](https://mlsanity.com/paper/2202.07282/) (2022)
- [Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Data](https://mlsanity.com/paper/1802.06300/) (2018)
- [Conformal prediction for time series](https://mlsanity.com/paper/2010.09107/) (2020)
- [An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls](https://mlsanity.com/paper/1712.09089/) (2017)
- [Valid model-free spatial prediction](https://mlsanity.com/paper/2006.15640/) (2020)
- [Anomalous Edge Detection in Edge Exchangeable Social Network Models](https://mlsanity.com/paper/2109.12727/) (2021)
- [Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains](https://mlsanity.com/paper/2210.02271/) (2022)
- [ResCP: Reservoir Conformal Prediction for Time Series Forecasting](https://mlsanity.com/paper/2510.05060/) (2025)
- [Conformal Inference for Time Series over Graphs](https://mlsanity.com/paper/2510.11049/) (2025)
- [Safe Merging in Mixed Traffic with Confidence](https://mlsanity.com/paper/2403.05742/) (2024)
- [Conformal Predictions under Markovian Data](https://mlsanity.com/paper/2407.15277/) (2024)
- [Adaptive Conformal Inference by Particle Filtering under Hidden Markov Models](https://mlsanity.com/paper/2411.01558/) (2024)
- [Bayesian hidden Markov models for latent variable labeling assignments in conflict research: application to the role ceasefires play in conflict dynamics](https://mlsanity.com/paper/2110.05475/) (2021)
- [Uncertainty quantification in automated valuation models with spatially weighted conformal prediction](https://mlsanity.com/paper/2312.06531/) (2023)
- [The 2023/24 VIEWS Prediction Challenge: Predicting the Number of Fatalities in Armed Conflict, with Uncertainty](https://mlsanity.com/paper/2407.11045/) (2024)
- [Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict](https://mlsanity.com/paper/2410.14507/) (2024)
- [Forecasting Densities of Fatalities from State-based Conflicts using Observed Markov Models](https://mlsanity.com/paper/2410.12374/) (2024)

## Abstract (arXiv)

Whether or not a country is at war, or experiencing escalating or deescalating levels of conflict, has massive ramifications on a country's national and foreign policy. Given a country's history of conflict, or lack thereof, future predictions about the war-status of a country are valuable information. In this paper, we present the use of conformal prediction on temporally-dependent data to obtain prediction sets of possible future conflict state-sequences. More specifically, we compare the results of conformal prediction to a likelihood-based prediction strategy when the data are assumed to come from a discrete-state Markov process. A point-prediction may not supply sufficient information because the penalty for a wrong prediction is extreme, and so we consider a machine learning alternative that gives valid uncertainty quantification and is robust to model misspecification. In the data analysis, we present real forecasts of conflict dynamics across multiple countries. Lastly, we comment on the possible limitations of existing approaches for applying conformal prediction to Markovian data, where the exchangeability assumption is violated.