Time-series Generative Adversarial Networks

Time-series Generative Adversarial NetworksJinsung Yoon University of California, Los Angeles, Jarrett University of Cambridge, van der SchaarUniversity of Cambridge, UKUniversity of California, Los Angeles, USAAlan Turing Institute, 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.

Adversarial Networks (TimeGAN), a natural framework for generating realistic time-series data in ... [16], biosignals [17], sensor [18] and smart grid data [19], as well as renewable scenarios [20]. Recent work [6] has proposed conditioning on time stamp information to 2. handle irregularly sampling. However, unlike our proposed technique ...

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