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I am very curious about the dimensions of data input. Take my data as an example, the dimension of training data is [240, 11], means there are 11 stations and each station has 240 data. Of cause, the dimension of the adjacent matrix is [11, 11]. Further, the dimension of testing data is [60, 11]. However, when I use the function 'predict', it raises an error:
'ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 2 dimension(s) and the array at index 1 has 1 dimension(s)'
and it happens at:
'predictions = np.concatenate((predictions, predict), axis=0) '.
can you help me handle this problem? Thank you very much!

Can you please provide us with a real world example even if a small dataset?

So, Is this still in development stage? Can you please tell if it doesn't work at all or you haven't handled some specific cases?