Motivated by real-world data of monthly values precipitation, minimum, and maximum temperature recorded at 360 monitoring stations covering the Italian territory for 60 years ( 12 × months), in this work we propose a change-point model multiple multivariate time series, inspired hierarchical Dirichlet process. We assume that each station has its structure and, as main novelties, allow unknown s...

Many events occur in the world. Some event types are stochastically excited or inhibited—in the sense of having their probabilities elevated or decreased—by patterns in the sequence of previous events. Discovering such patterns can help us predict which type of event will happen next and when. We propose to model streams of discrete events in continuous time, by constructing a neurally selfmodu...