diff --git a/aeon/transformations/series/_boxcox.py b/aeon/transformations/series/_boxcox.py
index 9f9a926c6b..6b670493c5 100644
--- a/aeon/transformations/series/_boxcox.py
+++ b/aeon/transformations/series/_boxcox.py
@@ -15,7 +15,7 @@
 
 
 # copy-pasted from scipy 1.7.3 since it moved in 1.8.0 and broke this estimator
-def _calc_uniform_order_statistic_medians(n):
+def _calc_uniform_order_statistic_medians(n: int) -> np.ndarray:
     """Approximations of uniform order statistic medians.
 
     Parameters
@@ -118,7 +118,7 @@ def __init__(self, bounds=None, method="mle", sp=None):
         self.sp = sp
         super().__init__(axis=1)
 
-    def _fit(self, X, y=None):
+    def _fit(self, X: np.ndarray, y=None) -> "BoxCoxTransformer":
         """
         Fit transformer to X and y.
 
@@ -143,7 +143,7 @@ def _fit(self, X, y=None):
 
         return self
 
-    def _transform(self, X, y=None):
+    def _transform(self, X: np.ndarray, y=None) -> np.ndarray:
         """Transform X and return a transformed version.
 
         private _transform containing the core logic, called from transform
@@ -164,7 +164,7 @@ def _transform(self, X, y=None):
         Xt = boxcox(X, self.lambda_)
         return Xt
 
-    def _inverse_transform(self, X, y=None):
+    def _inverse_transform(self, X: np.ndarray, y=None) -> np.ndarray:
         """Inverse transform X and return an inverse transformed version.
 
         core logic
@@ -185,7 +185,7 @@ def _inverse_transform(self, X, y=None):
         return Xt
 
 
-def _make_boxcox_optimizer(bounds=None, brack=(-2.0, 2.0)):
+def _make_boxcox_optimizer(bounds=None, brack: tuple = (-2.0, 2.0)):
     # bounds is None, use simple Brent optimisation
     if bounds is None:
 
@@ -206,7 +206,12 @@ def optimizer(func, args):
     return optimizer
 
 
-def _boxcox_normmax(x, bounds=None, brack=(-2.0, 2.0), method="pearsonr"):
+def _boxcox_normmax(
+    x: np.ndarray,
+    bounds=None,
+    brack: tuple = (-2.0, 2.0),
+    method: str = "pearsonr",
+):
     optimizer = _make_boxcox_optimizer(bounds, brack)
 
     def _pearsonr(x):
@@ -242,7 +247,7 @@ def _all(x):
     return optimfunc(x)
 
 
-def _guerrero(x, sp, bounds=None):
+def _guerrero(x: np.ndarray, sp: int, bounds=None):
     """Estimate lambda using the Guerrero method as described in [1]_.
 
     Parameters