However, some key architectural details can be clarified further for full reproducibility and analysis. Specifically: 1. How are historical observations combined with inputs known over all time given differences in sequence lengths (L vs L+M)? The text mentions separate embedding and addition with positional encoding, but clarifications on how the embeddings are combined and fed into the CSCM are needed. 2. Can each node attend to its own lower-level representation? From equation 2, it seems to be that only neighbouring nodes are attended to, based on the description of N_l^(s). 3. Do the authors have any guidelines on how to select S/A/C (and consequently N) for a given receptive field L?
In addition, while the ablation analysis tests the impact of changing CSCM architectures, it would be good to evaluate the base performance without the PAM to determine the value added by attention. This would also provide a simple comparison vs dilated CNNs which have been used successfully in time series forecasting applications (e.g. WaveNet).
Finally, could I double check which dataset was used for the ablation analysis as well? I seem to be having some difficulty lining the numbers in Tables 4-6 up with Table 3.
1. How are historical observations combined with inputs known over all time given differences in sequence lengths (L vs L+M)? The text mentions separate embedding and addition with positional encoding, but clarifications on how the embeddings are combined and fed into the CSCM are needed.