diff --git a/tods/detection_algorithm/core/MultiAutoRegOD.py b/tods/detection_algorithm/core/MultiAutoRegOD.py
index d1f6a07..9ef92b3 100644
--- a/tods/detection_algorithm/core/MultiAutoRegOD.py
+++ b/tods/detection_algorithm/core/MultiAutoRegOD.py
@@ -157,33 +157,6 @@ class MultiAutoRegOD(CollectiveBaseDetector):
         self._process_decision_scores()
         return self
 
-    def predict(self, X): # pragma: no cover
-        """Predict if a particular sample is an outlier or not.
-
-        Parameters
-        ----------
-        X : numpy array of shape (n_samples, n_features)
-            The input samples.
-
-        Returns
-        -------
-        outlier_labels : numpy array of shape (n_samples,)
-            For each observation, tells whether or not
-            it should be considered as an outlier according to the
-            fitted model. 0 stands for inliers and 1 for outliers.
-        """
-
-        check_is_fitted(self, ['decision_scores_', 'threshold_', 'labels_'])
-
-        pred_score, X_left_inds, X_right_inds = self.decision_function(X)
-
-        pred_score = np.concatenate((np.zeros((self.window_size,)), pred_score))
-        X_left_inds = np.concatenate((np.zeros((self.window_size,)), X_left_inds))
-        X_right_inds = np.concatenate((np.zeros((self.window_size,)), X_right_inds))
-
-        return (pred_score > self.threshold_).astype(
-            'int').ravel(), X_left_inds.ravel(), X_right_inds.ravel()
-
     def decision_function(self, X: np.array):
         """Predict raw anomaly scores of X using the fitted detector.
 
@@ -221,6 +194,12 @@ class MultiAutoRegOD(CollectiveBaseDetector):
         decison_mat_scaled = self._score_scalar.transform(decison_mat)
         decision_scores = self._score_combination(decison_mat_scaled)
 
+        # print(decision_scores.shape, X_left_inds.shape, X_right_inds.shape)
+        decision_scores = np.concatenate((np.zeros((self.window_size,)), decision_scores))
+        X_left_inds = np.concatenate(((-self.window_size)*np.ones((self.window_size,)).astype(np.int), X_left_inds))
+        X_right_inds = np.concatenate((np.zeros((self.window_size,)).astype(np.int), X_right_inds))
+        # print(decision_scores.shape, X_left_inds.shape, X_right_inds.shape)
+
         return decision_scores, X_left_inds, X_right_inds
 
 
diff --git a/tods/tests/detection_algorithm/test_AutoRegODetect.py b/tods/tests/detection_algorithm/test_AutoRegODetect.py
index e282b59..759aabc 100644
--- a/tods/tests/detection_algorithm/test_AutoRegODetect.py
+++ b/tods/tests/detection_algorithm/test_AutoRegODetect.py
@@ -87,21 +87,21 @@ class AutoRegODetectTestCase(unittest.TestCase):
             'metadata': {
                 'name': 'AutoRegODetector0_0',
                 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/Attribute'],
-                'structural_type': 'numpy.float64',
+                'structural_type': 'numpy.int64',
             },
         }, {
             'selector': ['__ALL_ELEMENTS__', 1],
             'metadata': {
                 'name': 'AutoRegODetector0_1',
                 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/Attribute'],
-                'structural_type': 'numpy.float64',
+                'structural_type': 'numpy.int64',
             },
         }, {
             'selector': ['__ALL_ELEMENTS__', 2],
             'metadata': {
                 'name': 'AutoRegODetector0_2',
                 'semantic_types': ['https://metadata.datadrivendiscovery.org/types/Attribute'],
-                'structural_type': 'numpy.float64',
+                'structural_type': 'numpy.int64',
             },
         }])