  In this study, we propose an approach for predicting rare events by
exploiting time series in coevolution. Our approach involves a weighted
autologistic regression model, where we leverage the temporal behavior of the
data to enhance predictive capabilities. By addressing the issue of imbalanced
datasets, we establish constraints leading to weight estimation and to improved
performance. Evaluation on synthetic and real-world datasets confirms that our
approach outperform state-of-the-art of predicting home equipment failure
methods.
