Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks
Abstract
Bayesian online changepoint detection (BOCPD) (Adams & MacKay, 2007) offers a rigorous and viable way to identify changepoints in complex systems. In this work, we introduce a Stein variational online changepoint detection (SVOCD) method to provide a computationally tractable generalization of BOCPD beyond the exponential family of probability distributions. We integrate the recently developed Stein variational Newton (SVN) method (Detommaso et al., 2018) and BOCPD to offer a full online Bayesian treatment for a large number of situations with significant importance in practice. We apply the resulting method to two challenging and novel applications: Hawkes processes and long short-term memory (LSTM) neural networks. In both cases, we successfully demonstrate the efficacy of our method on real data.
Keywords
Cite
@article{arxiv.1901.07987,
title = {Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks},
author = {Gianluca Detommaso and Hanne Hoitzing and Tiangang Cui and Ardavan Alamir},
journal= {arXiv preprint arXiv:1901.07987},
year = {2019}
}
Comments
14 pages, 6 figures