diff --git a/pts/transform/split.py b/pts/transform/split.py
index 789c445..0212cae 100644
--- a/pts/transform/split.py
+++ b/pts/transform/split.py
@@ -237,7 +237,7 @@ class CanonicalInstanceSplitter(FlatMapTransformation):
         instance sampler that provides sampling indices given a time-series
     instance_length
         length of the target seen before making prediction
-    output_NTC
+    batch_first
         whether to have time series output in (time, dimension) or in
         (dimension, time) layout
     time_series_fields
@@ -262,7 +262,7 @@ class CanonicalInstanceSplitter(FlatMapTransformation):
         forecast_start_field: str,
         instance_sampler: InstanceSampler,
         instance_length: int,
-        output_NTC: bool = True,
+        batch_first: bool = True,
         time_series_fields: List[str] = [],
         allow_target_padding: bool = False,
         pad_value: float = 0.0,
@@ -271,7 +271,7 @@ class CanonicalInstanceSplitter(FlatMapTransformation):
     ) -> None:
         self.instance_sampler = instance_sampler
         self.instance_length = instance_length
-        self.output_NTC = output_NTC
+        self.batch_first = batch_first
         self.dynamic_feature_fields = time_series_fields
         self.target_field = target_field
         self.allow_target_padding = allow_target_padding
@@ -341,14 +341,14 @@ class CanonicalInstanceSplitter(FlatMapTransformation):
                 else:
                     past_ts = full_ts[..., (i - self.instance_length) : i]
 
-                past_ts = past_ts.transpose() if self.output_NTC else past_ts
+                past_ts = past_ts.transpose() if self.batch_first else past_ts
                 d[self._past(ts_field)] = past_ts
 
                 if self.use_prediction_features and not is_train:
                     if not ts_field == self.target_field:
                         future_ts = full_ts[..., i : i + self.prediction_length]
                         future_ts = (
-                            future_ts.transpose() if self.output_NTC else future_ts
+                            future_ts.transpose() if self.batch_first else future_ts
                         )
                         d[self._future(ts_field)] = future_ts