diff --git a/experiments/reproduction.py b/experiments/reproduction.py
index 7055adb..a805e29 100644
--- a/experiments/reproduction.py
+++ b/experiments/reproduction.py
@@ -12,10 +12,12 @@
 
 def train():
     args = {"lr": 0.001, "weight_decay": 0.0001}
-    device = "mps"
+    device = "cuda"
     num_workers = 0  # NOTE Set to 0 for single thread debugging!
 
-    model = GraphWavenet(adaptive_embedding_dim=None, n_nodes=207, k_diffusion_hops=3)
+    model = GraphWavenet(
+        adaptive_embedding_dim=None, n_nodes=207, k_diffusion_hops=3, disable_gcn=True
+    )
     plmodule = GWnetForecasting(args, model, missing_value=0.0)
 
     tb_logger = pl_loggers.TensorBoardLogger(save_dir="logs/")
diff --git a/src/gwnet/callbacks/visualise_sequence_pred.py b/src/gwnet/callbacks/visualise_sequence_pred.py
index 36fb2ed..ec9ca71 100644
--- a/src/gwnet/callbacks/visualise_sequence_pred.py
+++ b/src/gwnet/callbacks/visualise_sequence_pred.py
@@ -47,7 +47,7 @@ def visualise_sequence(self, trainer: Trainer, pl_module: LightningModule) -> No
                     data.x[:, 0] = pl_module.scaler.inverse_transform(data.x[:, 0])
 
                 trainer.logger.experiment.add_scalar(
-                    "Sequence true", data.x[node_idx, 0, steps_ahead], t
+                    "Sequence true", data.y[node_idx, steps_ahead], t
                 )
 
             trainer.logger.experiment.add_scalar(
diff --git a/src/gwnet/model/gwnet.py b/src/gwnet/model/gwnet.py
index a67e2bd..794f073 100644
--- a/src/gwnet/model/gwnet.py
+++ b/src/gwnet/model/gwnet.py
@@ -138,6 +138,7 @@ def __init__(
         out_channels: int,
         dilation: int = 1,
         kernel_size: int = 2,
+        disable_gcn: bool = False,
     ):
         r"""
         Wraps the TCN and GCN modules.
@@ -146,9 +147,14 @@ def __init__(
             args (dict): Contains parameters passed from the parent module, namely
                 flags showing which adjacency matrices to expect and initialise
                 parameters for.
+
+            disable_gcn (bool): If True, the GCN aggregation will not be computed,
+                so effectively the model will have no graph component.
         """
         super().__init__()
 
+        self._disable_gcn = disable_gcn
+
         self.tcn = GatedTCN(
             in_channels, interm_channels, kernel_size=kernel_size, dilation=dilation
         )
@@ -167,11 +173,17 @@ def forward(self, x: Data, cached_adj: dict[str, torch.Tensor]) -> Data:
         to each other, and the node index matters.
 
         Args:
-            x: The batched Data object.
+            x (Data): The batched Data object.
+
+            cached_adj (dict): Adjacency matrices used in the GCN calculation.
         """
         # TCN works on the features alone, and handles the (N,C,L) shape
         # internally.
         tcn_out = self.tcn(x)
+
+        if self._disable_gcn:
+            return tcn_out
+
         return self.gcn(tcn_out, cached_adj)
 
 
@@ -191,6 +203,7 @@ def __init__(
         n_nodes: int | None = None,
         forward_diffusion: bool = True,
         backward_diffusion: bool = True,
+        disable_gcn: bool = False,
     ):
         r"""
         Initialise the GWnet model.
@@ -257,6 +270,7 @@ def __init__(
                         dilation_channels,
                         residual_channels,
                         dilation=dilation,
+                        disable_gcn=disable_gcn,
                     ),
                 )
             )