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Time-series Generative Adversarial Networks
Jinsung Yoon · Daniel Jarrett · Mihaela van der Schaar

Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #86

A good generative model for time-series data should preserve temporal dynamics, in the sense that new sequences respect the original relationships between variables across time. Existing methods that bring generative adversarial networks (GANs) into the sequential setting do not adequately attend to the temporal correlations unique to time-series data. At the same time, supervised models for sequence prediction - which allow finer control over network dynamics - are inherently deterministic. We propose a novel framework for generating realistic time-series data that combines the flexibility of the unsupervised paradigm with the control afforded by supervised training. Through a learned embedding space jointly optimized with both supervised and adversarial objectives, we encourage the network to adhere to the dynamics of the training data during sampling. Empirically, we evaluate the ability of our method to generate realistic samples using a variety of real and synthetic time-series datasets. Qualitatively and quantitatively, we find that the proposed framework consistently and significantly outperforms state-of-the-art benchmarks with respect to measures of similarity and predictive ability.

Author Information

Jinsung Yoon (University of California, Los Angeles)

I am a research scientist at Google Cloud AI. I am currently working on diverse machine learning research topics such as generative models, self- and semi-supervised learning, model interpretation, data imputation, and synthetic data generation. Previously, I worked on machine learning for medicine with Professor Mihaela van der Schaar as a graduate student researcher in UCLA Electrical and Computer Engineering Department. I received my Ph.D. and M.S. in Electrical and Computer Engineering Department at UCLA, and B.S. in Electrical and Computer Engineering at Seoul National University (SNU).

Daniel Jarrett (University of Cambridge)
Mihaela van der Schaar (University of California, Los Angeles)

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