Transcription of Time-series Generative Adversarial Networks
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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.
datum is real or synthetic; we can expressly learn from the transition dynamics from real sequences. Second, we introduce an embedding network to provide a reversible mapping between features and latent representations, thereby reducing the high-dimensionality of the adversarial learning space.
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