  Generating multivariate time series is a promising approach for sharing
sensitive data in many medical, financial, and IoT applications. A common type
of multivariate time series originates from a single source such as the
biometric measurements from a medical patient. This leads to complex dynamical
patterns between individual time series that are hard to learn by typical
generation models such as GANs. There is valuable information in those patterns
that machine learning models can use to better classify, predict or perform
other downstream tasks. We propose a novel framework that takes time series'
common origin into account and favors channel/feature relationships
preservation. The two key points of our method are: 1) the individual time
series are generated from a common point in latent space and 2) a central
discriminator favors the preservation of inter-channel/feature dynamics. We
demonstrate empirically that our method helps preserve channel/feature
correlations and that our synthetic data performs very well in downstream tasks
with medical and financial data.
