diff --git a/src/timesfm/patched_decoder.py b/src/timesfm/patched_decoder.py
index cd9d5f8..cdd81be 100644
--- a/src/timesfm/patched_decoder.py
+++ b/src/timesfm/patched_decoder.py
@@ -237,6 +237,7 @@ class PatchedTimeSeriesDecoder(base_layer.BaseLayer):
   stacked_transformer_params_tpl: LayerTpl = template_field(
       transformers.StackedTransformer)
   use_freq: bool = True
+  use_pos_emb: bool = True
 
   def setup(self) -> None:
     """Construct the model."""
@@ -332,16 +333,17 @@ def _preprocess_input(
     model_input = self.input_ff_layer(concat_inputs)
     # A patch should not be padded even if there is at least one zero.
     patched_padding = jnp.min(patched_pads, axis=-1)
-
-    if pos_emb is None:
-      position_emb = self.position_emb(seq_length=model_input.shape[1])
-    else:
-      position_emb = pos_emb
-    if self.do_eval:
-      if position_emb.shape[0] != model_input.shape[0]:
-        position_emb = jnp.repeat(position_emb, model_input.shape[0], axis=0)
-      position_emb = _shift_padded_seq(patched_padding, position_emb)
-    model_input += position_emb
+    
+    if self.use_pos_emb:
+      if pos_emb is None:
+        position_emb = self.position_emb(seq_length=model_input.shape[1])
+      else:
+        position_emb = pos_emb
+      if self.do_eval:
+        if position_emb.shape[0] != model_input.shape[0]:
+          position_emb = jnp.repeat(position_emb, model_input.shape[0], axis=0)
+        position_emb = _shift_padded_seq(patched_padding, position_emb)
+      model_input += position_emb
 
     return model_input, patched_padding, stats, patched_inputs
 
diff --git a/src/timesfm/timesfm_base.py b/src/timesfm/timesfm_base.py
index 230fb82..f2a8538 100644
--- a/src/timesfm/timesfm_base.py
+++ b/src/timesfm/timesfm_base.py
@@ -109,6 +109,7 @@ class TimesFmHparams:
   per_core_batch_size: int = 32
   backend: Literal["cpu", "gpu", "tpu"] = "cpu"
   quantiles: Sequence[float] | None = DEFAULT_QUANTILES
+  use_positional_embedding: bool = True
   # Hparams beyond the model.
   point_forecast_mode: Literal["mean", "median"] = "median"
 
@@ -172,6 +173,7 @@ def __init__(self, hparams: TimesFmHparams,
     self.backend = hparams.backend
     self.quantiles = hparams.quantiles
     self.num_heads = hparams.num_heads
+    self.use_pos_emb = hparams.use_positional_embedding
 
     # Rewrite these values in __post_init__ for SPMD.
     self.num_cores = 1
diff --git a/src/timesfm/timesfm_jax.py b/src/timesfm/timesfm_jax.py
index 1b4427f..cbd8576 100644
--- a/src/timesfm/timesfm_jax.py
+++ b/src/timesfm/timesfm_jax.py
@@ -117,6 +117,7 @@ def load_from_checkpoint(
         residual_block_tpl=pax_fiddle.Config(patched_decoder.ResidualBlock),
         quantiles=self.quantiles,
         use_freq=True,
+        use_pos_emb=self.use_pos_emb,
         stacked_transformer_params_tpl=pax_fiddle.Config(
             transformers.StackedTransformer,
             num_heads=self.num_heads,
diff --git a/src/timesfm/timesfm_torch.py b/src/timesfm/timesfm_torch.py
index d28a0b7..7bf298e 100644
--- a/src/timesfm/timesfm_torch.py
+++ b/src/timesfm/timesfm_torch.py
@@ -40,6 +40,7 @@ def __post_init__(self):
         horizon_len=self.output_patch_len,
         head_dim=self.model_dims // self.num_heads,
         quantiles=self.quantiles,
+        use_positional_embedding=self.use_pos_emb,
     )
     self._model = None
     self.num_cores = 1