A manufacturing line or utility network reports dozens of correlated signals at once — temperature, vibration, load, pressure. Generating synthetic data for just one signal at a time misses the point: the relationships between signals are often what a predictive model actually depends on.
How we approach it: model the temporal dynamics and the cross-signal correlation structure together, not as separate problems bolted together afterward. What it enables: multivariate synthetic data generation – the problem our OBGAN model was built to solve — and better predictive maintenance modeling downstream.
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