From: ~Ransalu_Senanayake1
comment: This paper proposes a temperature forecasting method using stacked LSTMs.

1. What did you learn from reading this paper that you did not know before?  
Although the main ideas of the paper are conveyed, the concepts proposed are not novel enough. 
2. What was the most exciting part of the work?
Stacking LSTM
3. What in your opinion makes this work stand apart from other papers in this area?
The comparison in Table 3. 
4. Does the paper fit into the scope of the workshop?
Yes
5. Are you aware of relevant literature that you may want to make the authors aware of?
One of the main problems of the paper is the lack of comparisons. Since this is purely an application paper, authors are expected to compare against benchmark datasets. It is essential to compare the proposed method with "convolutional LSTMs" as well as other temperature prediction algorithms. Authors might refer the seminal work by Shi et al "Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting" and Kim et al "DeepRain: ConvLSTM network for precipitation prediction using." 
On the other hand, stacking LSTMs is not a new idea--https://machinelearningmastery.com/stacked-long-short-term-memory-networks/

title: From: NIPS 2018 Workshop Spatiotemporal Paper34 AnonReviewer3

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper34/Authors
comment: Thank you for your feedback. Indeed what you mentioned regarding incorporating more spatial locations and exploring the benefits and drawbacks of combining hidden states in earlier or later layers when the number of layers is more than two, are interesting issues to investigate. Regarding combining hidden states in earlier or later layers, combining in later layers involves fewer parameters in comparison with combining in earlier layers. However, depending on the problem, one has to investigate if this smaller number of parameters can actually result in better performance.

Also, as you mention, in the results it can be seen that the data-driven method does not outperform the state-of-the-art methods in weather forecasting in all cases. However, the data that we are using for this paper is very limited in comparison with the one that is used by the other method and the performance is still competitive. For future work, we agree that it is interesting to check if adding more spatial and temporal data can result in better performance of the data-driven approach (in theory, as there are more data available, it is expected to perform better). 
title: Some answers to some of your concerns

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper34/AnonReviewer1
evaluation: Applying spatio-temporal stacked LSTM is novel in weather forecasting, and the paper had demonstrated a promising framework in such application. The overall flow of text and logic are easy to follow with helpful figures showing the architecture of the proposing structure. The two tables 1-2 given helped readers to distinguish the difference between a normal stacked LSTM and a spatio-temporal one. The benefit of a smaller number of parameters in comparison with stacked LSTM is also a promising advantage for small datasets, and is applicable for most applications with spatial and temporal data.

For the proposing framework, the paper further expands that the model is applicable for more than two layers, with the combination of hidden states happens any layer before the last LSTM layer. However, no further discussion is given for the benefit or trade-off for choosing the which layer to combine hidden states, e.g., how would the performance renders if the combination of hidden states happens in 'earlier' or 'later' layer. These might be appealing properties to understand how the LSTM is combining the spatial information, which could be further investigated.

The overall performance of the proposing framework does not seem to out-perform the state-of-the-art weather forecasting method. However, as a machine learning technique, it is a promising direction as it uses a data-driven approach which is much less computationally intensive than the current numerical method. It would be interesting to see how would the framework stands when applying more locations in the predictions, as there would be more spatial information to exploits on. The paper is bringing a more promising way to perform weather forcasting with data-driven method.

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper34/AnonReviewer1
relevance: 5: very high
impact: 3: borderline
novelty: 4: high