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Asynchronous event sequences are the basis of many applications throughoutdifferent industries. In this work, we tackle the task of predicting the nextevent (given a history), and how this prediction changes with the passage oftime. Since at some time points (e.g. predictions far into the future) we mightnot be able to predict anything with confidence, capturing uncertainty in thepredictions is crucial. We present two new architectures, WGP-LN and FD-Dir,modelling the evolution of the distribution on the probability simplex withtime-dependent logistic normal and Dirichlet distributions. In both cases, thecombination of RNNs with either Gaussian process or function decompositionallows to express rich temporal evolution of the distribution parameters, andnaturally captures uncertainty. Experiments on class prediction, timeprediction and anomaly detection demonstrate the high performances of ourmodels on various datasets compared to other approaches.

Uncertainty on Asynchronous Time Event Prediction