I saw several topics on how to feed a 3D tensor into a Neural Network but it’s not quite clear to me what’s happening.
I’m working with stock data and for each individual stock I have a 2 dimension torch containing several information (in columns) for each day. So, for each stock I have a matrix with features for column and days for rows and my NN works fine for a single stock. However, I’d like to apply the same operations for several stocks, thus yielding a 3d torch with the third dimension referring to each individual asset.
If I reshape the data into a 2d torch (stacking the features) I think i’ll lose information when trying to predict a single outcome with the trained NN. I’ve also found some topics explaining how to pass a 3d tensor to a Linear pass, but I understand they will make the Linear regression layer by layer, whereas I’d like to minimize the error for the whole 3d tensor.
Do you have any thoughts on that that might help me out?