text: This work addresses an important and outstanding problem: accurate long-term forecasting using deep recurrent networks. The technical approach seems well motivated, plausible, and potentially a good contribution, but the experimental work has numerous weaknesses which limit the significance of the work in current form. For one, the 3 datasets tested are not established as among the most suitable, well-recognized benchmarks for evaluating long-term forecasting. It would be far more convincing if the author’s used well-established benchmark data, for which existing best methods have already been well-tuned to get their best results. Otherwise, the reader is left with concerns that the author’s may not have used the best settings for the baseline method results reported, which indeed is a concern here (see below). One weakness with the experiments is that it is not clear that they were fair to RNN or LSTM, for example, in terms of giving them the same computation as the TT-RNNs. Section Hyper-parameter Analysis” on page 7 explains that they determined best TT rank and lags via grid search. But presumably larger values for rank and lag require more computation, so to be fair to RNN and LSTM they should be given more computation as well, for example allowing them more hidden units than TT-RNNs get, so that overall computation cost is the same for all 3 methods. As far as this reviewer can tell, the authors offer no experiments to show that a larger number of units for RNN or LSTM would not have helped them in improving long-term forecasting accuracies, so this seems like a very serious and plausible concern. Also, on page 6 the authors say that they tried ARMA but that it performed about 5% worse than LSTM, and thus dismissing direct comparisons of ARMA against TT-RNN. But they are unclear whether they gave ARMA as much hyper-parameter tuning (e.g. for number of lags) via grid search as their proposed TT-RNN benefited from. Again, the concern here is that the experiments are plausibly not being fair to all methods equally. So, due to the weaknesses in the experimental work, this work seems a bit premature. It should more clearly establish that their proposed TT-RNN are indeed performing well compared to existing SOTA.