diff --git a/dtaianomaly/anomaly_detection/BaseDetector.py b/dtaianomaly/anomaly_detection/BaseDetector.py
index 8b2a5ae..4ece293 100644
--- a/dtaianomaly/anomaly_detection/BaseDetector.py
+++ b/dtaianomaly/anomaly_detection/BaseDetector.py
@@ -46,7 +46,7 @@ def __init__(self, supervision: Supervision):
         self.supervision = supervision
 
     @abc.abstractmethod
-    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> 'BaseDetector':
+    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> 'BaseDetector':
         """
         Abstract method, fit this detector to the given data.
 
diff --git a/dtaianomaly/anomaly_detection/MedianMethod.py b/dtaianomaly/anomaly_detection/MedianMethod.py
index bb9a54c..6249aa5 100644
--- a/dtaianomaly/anomaly_detection/MedianMethod.py
+++ b/dtaianomaly/anomaly_detection/MedianMethod.py
@@ -67,7 +67,7 @@ def __init__(self, neighborhood_size_before: int, neighborhood_size_after: Optio
         self.neighborhood_size_before = neighborhood_size_before
         self.neighborhood_size_after = neighborhood_size_after
 
-    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> 'BaseDetector':
+    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> 'BaseDetector':
         return self  # No fitting is required.
 
     def decision_function(self, X: np.ndarray) -> np.ndarray:
diff --git a/dtaianomaly/anomaly_detection/baselines/baselines.py b/dtaianomaly/anomaly_detection/baselines/baselines.py
index 1e497b1..c82f5d8 100644
--- a/dtaianomaly/anomaly_detection/baselines/baselines.py
+++ b/dtaianomaly/anomaly_detection/baselines/baselines.py
@@ -15,7 +15,7 @@ class AlwaysNormal(BaseDetector):
     def __init__(self):
         super().__init__(Supervision.UNSUPERVISED)
 
-    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> 'AlwaysNormal':
+    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> 'AlwaysNormal':
         """
         Simply return this detector, because no fitting is required.
 
@@ -67,7 +67,7 @@ class AlwaysAnomalous(BaseDetector):
     def __init__(self):
         super().__init__(Supervision.UNSUPERVISED)
 
-    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> 'AlwaysAnomalous':
+    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> 'AlwaysAnomalous':
         """
         Simply return this detector, because no fitting is required.
 
@@ -126,7 +126,7 @@ def __init__(self, seed: Optional[int] = None):
         super().__init__(Supervision.UNSUPERVISED)
         self.seed = seed
 
-    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None) -> 'RandomDetector':
+    def fit(self, X: np.ndarray, y: Optional[np.ndarray] = None, **kwargs) -> 'RandomDetector':
         """
         Simply return this detector, because no fitting is required.
 
diff --git a/dtaianomaly/anomaly_detection/windowing_utils.py b/dtaianomaly/anomaly_detection/windowing_utils.py
index af2aeac..1e1a87a 100644
--- a/dtaianomaly/anomaly_detection/windowing_utils.py
+++ b/dtaianomaly/anomaly_detection/windowing_utils.py
@@ -192,7 +192,6 @@ def compute_window_size(
 
     # Use the fft to compute a window size
     elif window_size == 'fft':
-        print('REACHED')
         window_size_ = _dominant_fourier_frequency(X, lower_bound=lower_bound, upper_bound=upper_bound)
 
     # Use the acf to compute a window size