Reproduction of results not possible, due to errors in code.

[anshul013]

Respected authors,

Firstly, I would like to congratulate you on the publication of your paper named "From Similarity to Superiority: Channel Clustering
for Time Series Forecasting". I have been conducting some research in the field of Time Series Forecasting and I really liked your paper, so, I chose it as a baseline for my research.

But when I went to reproduce the results, I encountered 3 issues which were:

1. In the exp_ccm.py file under the similarity_loss_batch() method, you are multiplying the probability membership and similarity matrix but the similarity matrix is a tensor which results in an error.
2. The similarity and the membership matrix were not the same datatype.
3. In layers.py file, line 287 under the forward() method I received this error:
   prob = torch.mm(self.l2norm(x_emb), self.l2norm(cluster_emb).t()).reshape(bs, n_vars, self.n_cluster)
   RuntimeError: t() expects a tensor with <= 2 dimensions, but self is 3D

I fixed these errors and the code started working for me but the results were not even close to the reported results from the paper. Here are the results after running the code for ETTh1 for TSMixer model with the CCM module.
96 | 0.4652 | 0.4645
192 | 0.4162 | 0.4258
336 | 0.4530 | 0.4556
720 | 0.4981 | 0.5079

I would like to request you to please update your code to your final run which you utilised for the results that are reported in the research paper. I would also like to request you to please undertake the updation as soon as possible, as your implementation is quite integral for the research that I am carrying out. Thank you for your time and looking forward to hearing from you.

[CSyyn]

I had the same problem. Could you please update the final code?