Font Size: 

Forecasting multiple dependent zero-inflated count processes is a problem encountered in many statistical applications. Standard parametric approaches typically rely on independence assumptions that fail to capture dependence structures. Here a Bayesian nonparametric approach is proposed to overcome this problem and showcased on a real dataset of civil conflicts in Asia. The forecasting model is obtained by generalizing the clustering methods proposed in Franzolini et al. (2023).