Using time-varying information
The GFM(ML) used all the available features. So obviously, that model had access to a lot more information than the GFM(DL) we have built till now. The GFM(DL) we just built only takes in the history and nothing else. Let’s change that by including time-varying information. We will just use time-varying real features this time because dealing with categorical features is a topic I want to leave for the next section.
We initialize the training dataset the same way as before, but we add time_varying_known_reals=feat_config.time_varying_known_reals to the initialization parameters. Now that we have all the datasets created, let’s move on to setting up the model.
To set up the model, we need to understand one concept. We are now using the history of the target and time-varying known features. In Figure 15.3, we saw how TimeSeriesDataset arranges the different kinds of variables in PyTorch tensors. In the previous section, we used only...
