In evaluation, inverse min–max scaling is implemented as: `real = (event_time_non_mask[:,0,:].detach().cpu() + MIN[1]) * (MAX[1]-MIN[1])` `gen = (sampled_seq[:,0,:1].detach().cpu() + MIN[1]) * (MAX[1]-MIN[1])` and `real = (real + torch.tensor([MIN[2:]])) * (torch.tensor([MAX[2:]])-torch.tensor([MIN[2:]]))` `gen = (gen + torch.tensor([MIN[2:]])) * (torch.tensor([MAX[2:]])-torch.tensor([MIN[2:]]))` It shifts and scales wrongly, biasing MAE/RMSE and distance metrics. For min–max normalization `z = (x - MIN) / (MAX - MIN)`, denormalization is `x = z * (MAX - MIN) + MIN`