diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 33b84f4..c445178 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -215,6 +215,27 @@ def test_entropy_metric():
     assert spec_entropy_metric(D1) == 2.6402430161833763
 
 
+def test_shannon_entropy_metric():
+    ts = np.array([[[0, 2], [11, -11], [1, 2]], [[10, 21], [1, -1], [6, 8]]]).astype(np.float32)
+    y = np.array([1] * ts.shape[0])
+    D1 = tsgm.dataset.Dataset(ts, y=y)
+    sdi_metric = tsgm.metrics.ShannonEntropyMetric()
+    assert sdi_metric(D1) == 0
+    y = np.array([1, 2])
+    D2 = tsgm.dataset.Dataset(ts, y=y)
+    assert sdi_metric(D2) > 0
+
+
+def test_pairwise_distance_metric():
+    ts = np.array([[[0, 2], [11, -11], [1, 2]], [[0, 2], [11, -11], [1, 2]]]).astype(np.float32)
+    D1 = tsgm.dataset.Dataset(ts, y=None)
+    pd_metric = tsgm.metrics.PairwiseDistanceMetric()
+    assert np.mean(pd_metric(D1)) == 0
+    ts = np.array([[[0, 2], [11, -11], [1, 2]], [[10, 21], [1, -1], [6, 8]]]).astype(np.float32)
+    D2 = tsgm.dataset.Dataset(ts, y=None)
+    assert np.mean(pd_metric(D2)) > 0
+
+
 def test_demographic_parity():
     ts = np.array([[[0, 2], [11, -11], [1, 2]], [[0, 2], [11, -11], [1, 2]], [[10, 21], [1, -1], [6, 8]]]).astype(np.float32)
     y = np.array([0, 1, 1])
diff --git a/tsgm/metrics/__init__.py b/tsgm/metrics/__init__.py
index 926c637..c01851c 100644
--- a/tsgm/metrics/__init__.py
+++ b/tsgm/metrics/__init__.py
@@ -2,5 +2,6 @@
 from tsgm.metrics.metrics import (
     DistanceMetric, ConsistencyMetric, BaseDownstreamEvaluator,
     DownstreamPerformanceMetric, PrivacyMembershipInferenceMetric,
-    MMDMetric, DiscriminativeMetric, EntropyMetric, DemographicParityMetric
+    MMDMetric, DiscriminativeMetric, EntropyMetric, DemographicParityMetric,
+    ShannonEntropyMetric, PairwiseDistanceMetric
 )
diff --git a/tsgm/metrics/metrics.py b/tsgm/metrics/metrics.py
index afd3776..009e923 100644
--- a/tsgm/metrics/metrics.py
+++ b/tsgm/metrics/metrics.py
@@ -6,7 +6,9 @@
 import itertools
 import sklearn
 import scipy
+from scipy.stats import entropy
 from tqdm import tqdm
+from scipy.spatial.distance import pdist, squareform
 from tensorflow.python.types.core import TensorLike
 
 import tsgm
@@ -304,7 +306,7 @@ def _spectral_entropy_sum(X: TensorLike) -> TensorLike:
 
 class EntropyMetric(Metric):
     """
-    Calculates the spectral entropy of a dataset or tensor.
+    Calculates the spectral entropy of a dataset or tensor as a sum of individual entropies.
 
     Args:
         d (tsgm.dataset.DatasetOrTensor): The input dataset or tensor.
@@ -330,6 +332,76 @@ def __call__(self, d: tsgm.dataset.DatasetOrTensor) -> float:
         """
         X = _dataset_or_tensor_to_tensor(d)
         return np.sum(_spectral_entropy_sum(X), axis=None)
+    
+
+class ShannonEntropyMetric(Metric):
+    """
+    Shannon Entropy calculated over the labels of a dataset.
+    This index is a measure of diversity that accounts for categories present in a dataset.
+    """
+
+    def _shannon_entropy(self, labels):
+        """
+        Private method to calculate the Shannon Entropy for a given set of labels.
+
+        Parameters:
+        labels (array-like): The labels or categories for which the diversity measure is to be calculated.
+
+        Returns:
+        float: The Shannon Entropy value.
+        """
+        _, counts = np.unique(labels, return_counts=True)
+        return entropy(counts)
+
+    def __call__(self, d: tsgm.dataset.DatasetOrTensor) -> float:
+        """
+        Calculate the Shannon entropy for the dataset.
+
+        Parameters:
+        d (tsgm.dataset.DatasetOrTensor): The dataset or tensor object containing the labels.
+
+        Returns:
+        float: The Shannon entropy value.
+
+        Raises:
+        AssertionError: If the dataset does not contain labels.
+        """
+        y = d.y
+        assert y is not None, "The dataset must contain labels."
+
+        return self._shannon_entropy(y)
+    
+
+class PairwiseDistanceMetric(Metric):
+    """
+    Measures pairwise distances in a set of time series.
+    """
+
+    def pairwise_euclidean_distances(self, ts: TensorLike) -> TensorLike:
+        """
+        Computes the pairwise Euclidean distances for a set of time series.
+
+        Parameters:
+        ts (numpy.ndarray): A 2D array where each row represents a time series.
+
+        Returns:
+        numpy.ndarray: A 2D array representing the pairwise Euclidean distance matrix.
+        """
+        distances = pdist(np.reshape(ts, (ts.shape[0], -1)), metric='euclidean')
+        return squareform(distances)
+
+    def __call__(self, d: tsgm.dataset.DatasetOrTensor) -> TensorLike:
+        """
+        Calculates the pairwise Euclidean distances for a dataset or tensor.
+
+        Parameters:
+        d (tsgm.dataset.DatasetOrTensor): The input dataset or tensor containing time series data.
+
+        Returns:
+        float: The pairwise Euclidean distances of the input data.
+        """
+        X = _dataset_or_tensor_to_tensor(d)
+        return self.pairwise_euclidean_distances(X)
 
 
 class DemographicParityMetric(Metric):