Time-series Generative Adversarial Networks

A good generative model for time-series data should preservetemporal dynamics,in the sense that new sequences respect the original relationships between variablesacross time. Existing methods that bring generative adversarial networks (GANs)into the sequential setting do not adequately attend to the temporal correlationsunique to time-series data. At the same time, supervised models for sequenceprediction—which allow finer control over network dynamics—are inherentlydeterministic. We propose a novel framework for generating realistic time-seriesdata that combines the flexibility of the unsupervised paradigm with the controlafforded by supervised training. Through a learned embedding space jointlyoptimized with both supervised and adversarial objectives, we encourage thenetwork 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 ofreal and synthetic time-series datasets. Qualitatively and quantitatively, we find thatthe proposed framework consistently and significantly outperforms state-of-the-artbenchmarks with respect to measures of similarity and predictive ability.