Wildfire modeling with point process and conformal prediction

Due to significant societal and environmental impacts, obtaining a more informed understanding of wildfire activities is always important. This work uses historical data to focus on wildfire pattern recognition, prediction, and subsequent uncertainty quantification. We propose an interpretable and flexible marked spatio-temporal point process model to accomplish the tasks and adopt recent advances in time-series conformal prediction. Through extensive real-data experiments, we demonstrate the effectiveness of our methods against competing baselines.