I have a dataset, where I simulate 8 different households, and if they walked

outside that day. The next day, other people will leave their houses. They are

impacted by the other people, and the different impacts are stored in a matrix

(W=8x8). The people outside one day (in a 8 element 0/1 vector) is then

multiplied with the matrix, to get the next day. This is then simulated for

many time steps, and I also have many simulations. One simulation is in the

shape (1000 x 8), where there are 1000 time steps.

My goal is to reconstruct the matrix from the time series data. I have thought

of using a transformer based approach, and feed in only chunks from the

simulation data, eg. 32x8 from the simulation.

I have found the `torch.nn.Transformer()`

, but I donâ€™t understand what src and tgt

are supposed to be.

Also: Are transformer-based methods the correct architecture to attack this problem?