A team needs more time-series data than they have — either there isn’t enough history, or the real data is too sensitive to use directly for model development. Off-the-shelf generative models often produce sequences that look plausible for a few steps and then drift or collapse into unrealistic patterns.
How we approach it: generate synthetic time series using generative architectures specifically stabilized for sequential data, rather than adapting an image-generation GAN and hoping it holds up. What it enables: the kind of stable univariate signal generation our BifGAN model was built for – safe, realistic training data without exposing sensitive originals.
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