add 0 padding to targets, to compensate for missing past covariates

Author: JanFidor

darts/models/forecasting/transformer_model.py

@@ -295,26 +295,28 @@ def forward(self, x_in: Tuple):
         """
         data = x_in[0]
 
-        start_token = self._permute_transformer_inputs(data[:, -1:, :])
+        pad_size = (0, self.input_size - self.target_size)
+        start_token = self._permute_transformer_inputs(data[:, -1:, : self.target_size])
+        start_token_padded = F.pad(start_token, pad_size)
         if len(x_in) == 3:
             src, _, tgt = x_in
             src = self._permute_transformer_inputs(src)
             tgt_permuted = self._permute_transformer_inputs(tgt)
-            tgt_padded = F.pad(tgt_permuted, (0, self.input_size - self.target_size))
-            tgt = torch.cat([start_token, tgt_padded], dim=0)
+            tgt_padded = F.pad(tgt_permuted, pad_size)
+            tgt = torch.cat([start_token_padded, tgt_padded], dim=0)
             return self._prediction_step(src, tgt)[:, :-1, :, :]
 
         data, _ = x_in
 
         src = self._permute_transformer_inputs(data)
-        tgt = start_token
+        tgt = start_token_padded
 
         predictions = []
         for _ in range(self.output_chunk_length):
             pred = self._prediction_step(src, tgt)[:, -1, :, :]
             predictions.append(pred)
             tgt = torch.cat(
-                [tgt, pred.mean(dim=2).unsqueeze(dim=0)],
+                [tgt, F.pad(pred.mean(dim=2).unsqueeze(dim=0), pad_size)],
                 dim=0,
             )  # take average of quantiles
         return torch.stack(predictions, dim=1)