Thanks for your reply!
Is the L - sequence length a concept in NLP? The task of my LSTM is the prediction for the time series. ‘3’ means the three Tensors torch.cat() by me. ‘H*W’ means the all points in my dataset. ‘T’ means the timesteps in the train window. I think H*W correspond to N because the prediction is for every single point. And ‘T’ correspond to ‘H_in’ because I want to map it to the hidden state. How should I understand the L? 3 is just the demension torch.cat() by three Tensors, it can be any number as I add some Conv2D(I also have plans to try this operation in the future).
In fact, my code has achieved the best results I have ever gotten, and it has already met my expectations.
        linputs = torch.cat((hinputs, hinputs2, inputs), 1)
        llabels = torch.cat((hlabels, hlabels2, tlabels), 1)

is better than
        linputs = torch.cat((hinputs2, hinputs, inputs), 1)
        llabels = torch.cat((hlabels2, hlabels, tlabels), 1)

and
        linputs = torch.cat((inputs, hinputs, hinputs2), 1)
        llabels = torch.cat((tlabels, hlabels, hlabels2), 1)
        #or this
        #linputs = torch.cat((inputs, hinputs2, hinputs), 1)
        #llabels = torch.cat((tlabels, hlabels2, hlabels), 1)

In the absence of any changes in other codes, poor results were obtained that were completely out of line with expectations.
Therefore, the possible reason is that the dimension by torch.cat() is used as L, which causes these three Tensors to play different roles in the calculation of LSTM, which leads to the different predictions, right?