Uncertainty on Asynchronous Time Event Prediction

TL;DR

This paper introduces two novel architectures, WGP-LN and FD-Dir, for predicting asynchronous event sequences while effectively modeling and capturing uncertainty over time, improving performance in various predictive tasks.

Contribution

The work presents new models combining RNNs with Gaussian processes and function decomposition to capture temporal uncertainty in event prediction.

Findings

High performance on class prediction tasks

Effective in time prediction and anomaly detection

Models outperform existing approaches across datasets

Abstract

Asynchronous event sequences are the basis of many applications throughout different industries. In this work, we tackle the task of predicting the next event (given a history), and how this prediction changes with the passage of time. Since at some time points (e.g. predictions far into the future) we might not be able to predict anything with confidence, capturing uncertainty in the predictions is crucial. We present two new architectures, WGP-LN and FD-Dir, modelling the evolution of the distribution on the probability simplex with time-dependent logistic normal and Dirichlet distributions. In both cases, the combination of RNNs with either Gaussian process or function decomposition allows to express rich temporal evolution of the distribution parameters, and naturally captures uncertainty. Experiments on class prediction, time prediction and anomaly detection demonstrate the high performances of our models on various datasets compared to other approaches.