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Generating multivariate time series is a promising approach for sharingsensitive data in many medical, financial, and IoT applications. A common typeof multivariate time series originates from a single source such as thebiometric measurements from a medical patient. This leads to complex dynamicalpatterns between individual time series that are hard to learn by typicalgeneration models such as GANs. There is valuable information in those patternsthat machine learning models can use to better classify, predict or performother downstream tasks. We propose a novel framework that takes time series'common origin into account and favors channel/feature relationshipspreservation. The two key points of our method are: 1) the individual timeseries are generated from a common point in latent space and 2) a centraldiscriminator favors the preservation of inter-channel/feature dynamics. Wedemonstrate empirically that our method helps preserve channel/featurecorrelations and that our synthetic data performs very well in downstream taskswith medical and financial data.

Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN)