diff --git a/perceiverar/estimator.py b/perceiverar/estimator.py
index 144512d..9f3d93e 100644
--- a/perceiverar/estimator.py
+++ b/perceiverar/estimator.py
@@ -131,6 +131,7 @@ class PerceiverAREstimator(PyTorchLightningEstimator):
         prediction_length: int,
         depth: int,
         context_length: Optional[int] = None,
+        input_size: int = 1,
         perceive_depth: int = 1,
         heads: int = 2,
         hidden_size: int = 32,
@@ -163,6 +164,7 @@ class PerceiverAREstimator(PyTorchLightningEstimator):
             default_trainer_kwargs.update(trainer_kwargs)
         super().__init__(trainer_kwargs=default_trainer_kwargs)
 
+        self.input_size = input_size
         self.freq = freq
         self.context_length = (
             context_length if context_length is not None else prediction_length
@@ -341,6 +343,7 @@ class PerceiverAREstimator(PyTorchLightningEstimator):
 
     def create_lightning_module(self) -> PerceiverARLightningModule:
         model = PerceiverARModel(
+            input_size=self.input_size,
             freq=self.freq,
             depth=self.depth,
             context_length=self.context_length,
diff --git a/perceiverar/module.py b/perceiverar/module.py
index 5267d8f..4c87805 100644
--- a/perceiverar/module.py
+++ b/perceiverar/module.py
@@ -236,6 +236,7 @@ class PerceiverARModel(nn.Module):
         num_feat_static_cat: int,
         cardinality: List[int],
         embedding_dimension: Optional[List[int]] = None,
+        input_size: int = 1,
         perceive_depth: int = 1,
         heads: int = 2,
         perceive_max_heads_process: int = 2,
@@ -250,6 +251,7 @@ class PerceiverARModel(nn.Module):
     ) -> None:
         super().__init__()
 
+        self.input_size = input_size
         self.context_length = context_length
         self.prediction_length = prediction_length
         self.distr_output = distr_output
@@ -274,7 +276,7 @@ class PerceiverARModel(nn.Module):
         else:
             self.scaler = NOPScaler(dim=1, keepdim=True)
 
-        dim_head = len(self.lags_seq) + self._number_of_features
+        dim_head = input_size * len(self.lags_seq) + self._number_of_features
 
         self.perceive_layers = nn.ModuleList([])
         for _ in range(perceive_depth):
@@ -315,7 +317,7 @@ class PerceiverARModel(nn.Module):
             sum(self.embedding_dimension)
             + self.num_feat_dynamic_real
             + self.num_feat_static_real
-            + 1  # the log(scale)
+            + self.input_size  # the log(scale)
         )
 
     @property
@@ -387,8 +389,9 @@ class PerceiverARModel(nn.Module):
         )
 
         embedded_cat = self.embedder(feat_static_cat)
+        log_scale = scale.log() if self.input_size == 1 else scale.squeeze(1).log()
         static_feat = torch.cat(
-            (embedded_cat, feat_static_real, scale.log()),
+            (embedded_cat, feat_static_real, log_scale),
             dim=1,
         )
         expanded_static_feat = static_feat.unsqueeze(1).expand(-1, input.shape[1], -1)