Deep Bayesian Nonparametric Tracking

Aonan Zhang, John Paisley
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:5833-5841, 2018.

Abstract

Time-series data often exhibit irregular behavior, making them hard to analyze and explain with a simple dynamic model. For example, information in social networks may show change-point-like bursts that then diffuse with smooth dynamics. Powerful models such as deep neural networks learn smooth functions from data, but are not as well-suited (in off-the-shelf form) for discovering and explaining sparse, discrete and bursty dynamic patterns. Bayesian models can do this well by encoding the appropriate probabilistic assumptions in the model prior. We propose an integration of Bayesian nonparametric methods within deep neural networks for modeling irregular patterns in time-series data. We use a Bayesian nonparametrics to model change-point behavior in time, and a deep neural network to model nonlinear latent space dynamics. We compare with a non-deep linear version of the model also proposed here. Empirical evaluations demonstrates improved performance and interpretable results when tracking stock prices and Twitter trends.

Cite this Paper

BibTeX
@InProceedings{pmlr-v80-zhang18j, title = {Deep {B}ayesian Nonparametric Tracking}, author = {Zhang, Aonan and Paisley, John}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, pages = {5833--5841}, year = {2018}, editor = {Dy, Jennifer and Krause, Andreas}, volume = {80}, series = {Proceedings of Machine Learning Research}, month = {10--15 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v80/zhang18j/zhang18j.pdf}, url = {https://proceedings.mlr.press/v80/zhang18j.html}, abstract = {Time-series data often exhibit irregular behavior, making them hard to analyze and explain with a simple dynamic model. For example, information in social networks may show change-point-like bursts that then diffuse with smooth dynamics. Powerful models such as deep neural networks learn smooth functions from data, but are not as well-suited (in off-the-shelf form) for discovering and explaining sparse, discrete and bursty dynamic patterns. Bayesian models can do this well by encoding the appropriate probabilistic assumptions in the model prior. We propose an integration of Bayesian nonparametric methods within deep neural networks for modeling irregular patterns in time-series data. We use a Bayesian nonparametrics to model change-point behavior in time, and a deep neural network to model nonlinear latent space dynamics. We compare with a non-deep linear version of the model also proposed here. Empirical evaluations demonstrates improved performance and interpretable results when tracking stock prices and Twitter trends.} }
Endnote
%0 Conference Paper %T Deep Bayesian Nonparametric Tracking %A Aonan Zhang %A John Paisley %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr-v80-zhang18j %I PMLR %P 5833--5841 %U https://proceedings.mlr.press/v80/zhang18j.html %V 80 %X Time-series data often exhibit irregular behavior, making them hard to analyze and explain with a simple dynamic model. For example, information in social networks may show change-point-like bursts that then diffuse with smooth dynamics. Powerful models such as deep neural networks learn smooth functions from data, but are not as well-suited (in off-the-shelf form) for discovering and explaining sparse, discrete and bursty dynamic patterns. Bayesian models can do this well by encoding the appropriate probabilistic assumptions in the model prior. We propose an integration of Bayesian nonparametric methods within deep neural networks for modeling irregular patterns in time-series data. We use a Bayesian nonparametrics to model change-point behavior in time, and a deep neural network to model nonlinear latent space dynamics. We compare with a non-deep linear version of the model also proposed here. Empirical evaluations demonstrates improved performance and interpretable results when tracking stock prices and Twitter trends.
APA
Zhang, A. & Paisley, J.. (2018). Deep Bayesian Nonparametric Tracking. Proceedings of the 35th International Conference on Machine Learning, in Proceedings of Machine Learning Research 80:5833-5841 Available from https://proceedings.mlr.press/v80/zhang18j.html.

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