(Conditional) generative adversarial networks (GANs) have had great success in recent years, due to their ability to approximate (conditional) distributions over extremely high-dimensional spaces.
Consider the situation when we have training data containing many time series having known group membership and testing data with unknown group membership. The goals are to find timescale features ...
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