text: This paper proposes to use RGANs and RCGANS to generate synthetic sequences of actual data.  They demonstrate the quality of the sequences on sine waves, MNIST, and ICU telemetry data. The authors demonstrate novel approaches for generating real-valued sequences using adversarial training, a train on synthetic, test of real and vice versa method for evaluating GANS, generating synthetic medical time series data, and an empirical privacy analysis.  Major - the medical use case is not motivating.  de-identifying the 4 telemetry measures is extremely easy  and there is little evidence to show that it is even possible to reidentify individuals using these 4 measures.  our institutional review board would certainly allow self-certification of the data (i.e. removing the patient identifiers and publishing the first 4 hours of sequences). - the labels selected by the authors for the icu example are to forecast the next 15 minutes and whether a critical value is reached.  Please add information about how this critical value was generated.  Also it would be very useful to say that a physician was consulted and that the critical values were "clinically" useful. - the changes in performance of TSTR are large enough that I would have difficulty trusting any experiments using the synthetic data.  If I optimized a method using this synthetic data, I would still need to assess the result on real data. - In addition it is unclear whether this synthetic process would actually generate results that are clinically useful.  The authors certainly make a convincing statement about the internal validity of the method.  An externally valid measure would strengthen the results.  I'm not quite sure how the authors could externally validate the synthetic data  as this would also require generating synthetic outcome measures.  I think it would be possible for the synthetic sequence to also generate an outcome measure (i.e. death) based on the first 4 hours of stay. Minor- write in the description for table 1 what task the accuracies correspond. Summary The authors present methods for generating synthetic sequences.  The MNIST example is compelling.  However the ICU example has some pitfalls which need to be addressed.