Closed Tolga-Karahan closed 2 years ago
Or the point is getting a subset for training and another subset that immediately follow the training subset for forecasting and for each new idx window is shifted to right?
Hi, thanks for your comment. As you said, it predicts the window shifted once to the right.
src = _input.permute(1,0,2).double().to(device)[:-1,:,:]
target = _input.permute(1,0,2).double().to(device)[1:,:,:] # src shifted by 1.
prediction = model(src, device)
loss = criterion(prediction, target[:,:,0].unsqueeze(-1))
Thanks for sharing this inspiring work. When I was inspecting your SensorDataset class I couldn't understand the part you're creating input and target variables. For input this indices are used: [start : start + self.T], and for target this ones: [start + self.T : start + self.T + self.S]. Shouldn't model output next timestamp for each input so for example if input indices start with n. timestamp then target must start with (n+1). timestamp, but I wonder whether it's not the case for your work?