diff --git a/sdmetrics/timeseries/__init__.py b/sdmetrics/timeseries/__init__.py
index 6a09b529..bc012092 100644
--- a/sdmetrics/timeseries/__init__.py
+++ b/sdmetrics/timeseries/__init__.py
@@ -5,6 +5,7 @@
 from sdmetrics.timeseries.detection import LSTMDetection, TimeSeriesDetectionMetric
 from sdmetrics.timeseries.efficacy import TimeSeriesEfficacyMetric
 from sdmetrics.timeseries.efficacy.classification import LSTMClassifierEfficacy
+from sdmetrics.timeseries.sequence_length_similarity import SequenceLengthSimilarity
 
 __all__ = [
     'base',
@@ -16,4 +17,5 @@
     'LSTMDetection',
     'TimeSeriesEfficacyMetric',
     'LSTMClassifierEfficacy',
+    'SequenceLengthSimilarity',
 ]
diff --git a/sdmetrics/timeseries/sequence_length_similarity.py b/sdmetrics/timeseries/sequence_length_similarity.py
index 1260f428..45e500a9 100644
--- a/sdmetrics/timeseries/sequence_length_similarity.py
+++ b/sdmetrics/timeseries/sequence_length_similarity.py
@@ -20,7 +20,7 @@ class SequenceLengthSimilarity:
             Maximum value or values that this metric can take.
     """
 
-    name = 'BayesianNetwork Likelihood'
+    name = 'Sequence Length Similarity'
     goal = Goal.MAXIMIZE
     min_value = 0.0
     max_value = 1.0
@@ -50,7 +50,14 @@ def compute(real_data: pd.Series, synthetic_data: pd.Series) -> float:
             float:
                 Mean of the log probabilities returned by the Bayesian Network.
         """
-        real_lengths = real_data.value_counts().to_numpy()
-        synthetic_lengths = synthetic_data.value_counts().to_numpy()
+        real_lengths = real_data.value_counts(sort=False)
+        synthetic_lengths = synthetic_data.value_counts(sort=False)
+
+        all_indexes = real_lengths.index.union(synthetic_lengths.index)
+        real_lengths = real_lengths.reindex(all_indexes, fill_value=0)
+        synthetic_lengths = synthetic_lengths.reindex(all_indexes, fill_value=0)
+
+        real_lengths = real_lengths.sort_index()
+        synthetic_lengths = synthetic_lengths.sort_index()
 
         return KSComplement.compute(real_lengths, synthetic_lengths)
diff --git a/tests/unit/timeseries/test_sequence_length_similarity.py b/tests/unit/timeseries/test_sequence_length_similarity.py
index 96f59e81..ab377faa 100644
--- a/tests/unit/timeseries/test_sequence_length_similarity.py
+++ b/tests/unit/timeseries/test_sequence_length_similarity.py
@@ -19,11 +19,23 @@ def test_compute_one(self):
     def test_compute_low_score(self):
         """Test it for distinct distributions."""
         # Setup
-        real_data = pd.Series(['id1', 'id1', 'id2'])
-        synthetic_data = pd.Series(['id1', 'id2', 'id3'])
+        real_data = pd.Series([f'id{i}' for i in range(100)])
+        synthetic_data = pd.Series(['id1'] * 100)
 
         # Run
         score = SequenceLengthSimilarity.compute(real_data, synthetic_data)
 
         # Assert
-        assert score == 0.5
+        assert score == 0.010000000000000009
+
+    def test_compute_one_difference_sequences(self):
+        """Test it returns one for distinct distributions when they are sorted."""
+        # Setup
+        real_data = pd.Series(['id1', 'id1', 'id1'])
+        synthetic_data = pd.Series(['id2', 'id2', 'id2'])
+
+        # Run
+        score = SequenceLengthSimilarity.compute(real_data, synthetic_data)
+
+        # Assert
+        assert score == 1