Uncertainty on Asynchronous Time Event Prediction
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)...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
13.11.2019
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.1911.05503 |