diff --git a/pytorch_forecasting/data/timeseries.py b/pytorch_forecasting/data/timeseries.py
index 336eecd5f..afe465877 100644
--- a/pytorch_forecasting/data/timeseries.py
+++ b/pytorch_forecasting/data/timeseries.py
@@ -2513,7 +2513,7 @@ def to_dataloader(
 
         Parameters
         ----------
-        train : bool, optional, default=Trze
+        train : bool, optional, default=True
             whether dataloader is used for training (True) or prediction (False).
             Will shuffle and drop last batch if True. Defaults to True.
         batch_size : int, optional, default=64
diff --git a/pytorch_forecasting/utils/_utils.py b/pytorch_forecasting/utils/_utils.py
index af93006cf..364c74d68 100644
--- a/pytorch_forecasting/utils/_utils.py
+++ b/pytorch_forecasting/utils/_utils.py
@@ -272,7 +272,33 @@ def concat_sequences(
     if isinstance(sequences[0], rnn.PackedSequence):
         return rnn.pack_sequence(sequences, enforce_sorted=False)
     elif isinstance(sequences[0], torch.Tensor):
-        return torch.cat(sequences, dim=1)
+        if sequences[0].ndim > 1:
+            first_lens = [xi.shape[1] for xi in sequences]
+            max_first_len = max(first_lens)
+            if max_first_len > min(first_lens):
+                sequences = [
+                    (
+                        xi
+                        if xi.shape[1] == max_first_len
+                        else torch.cat(
+                            [
+                                xi,
+                                torch.full(
+                                    (
+                                        xi.shape[0],
+                                        max_first_len - xi.shape[1],
+                                        *xi.shape[2:],
+                                    ),
+                                    float("nan"),
+                                    device=xi.device,
+                                ),
+                            ],
+                            dim=1,
+                        )
+                    )
+                    for xi in sequences
+                ]
+        return torch.cat(sequences, dim=0)
     elif isinstance(sequences[0], (tuple, list)):
         return tuple(
             concat_sequences([sequences[ii][i] for ii in range(len(sequences))])
diff --git a/tests/test_models/test_temporal_fusion_transformer.py b/tests/test_models/test_temporal_fusion_transformer.py
index 24c249bd5..5cbf51909 100644
--- a/tests/test_models/test_temporal_fusion_transformer.py
+++ b/tests/test_models/test_temporal_fusion_transformer.py
@@ -203,10 +203,19 @@ def _integration(dataloader, tmp_path, loss=None, trainer_kwargs=None, **kwargs)
                 return_index=True,
                 return_x=True,
                 return_y=True,
-                fast_dev_run=True,
+                fast_dev_run=2,
                 trainer_kwargs=trainer_kwargs,
             )
             pred_len = len(predictions.index)
+            if isinstance(predictions.output, torch.Tensor):
+                assert (
+                    predictions.output.shape == predictions.y[0].shape
+                ), "shape of predictions should match shape of targets"
+            else:
+                for i in range(len(predictions.output)):
+                    assert (
+                        predictions.output[i].shape == predictions.y[0][i].shape
+                    ), "shape of predictions should match shape of targets"
 
             # check that output is of correct shape
             def check(x):