diff --git a/CHANGELOG.md b/CHANGELOG.md
index 57c22e2ee..7b25ede12 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,6 +6,12 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ## [Unreleased]
+### Added
+- RelevanceTable returns rank ([#268](https://github.com/tinkoff-ai/etna-ts/pull/268/))
+
+### Changed
+
+### Fixed
 
 ## [1.3.1] - 2021-11-12
 ### Changed
diff --git a/etna/analysis/feature_relevance/relevance.py b/etna/analysis/feature_relevance/relevance.py
index eceaa9421..61844a39a 100644
--- a/etna/analysis/feature_relevance/relevance.py
+++ b/etna/analysis/feature_relevance/relevance.py
@@ -2,6 +2,7 @@
 from abc import abstractmethod
 
 import pandas as pd
+import scipy.stats
 
 from etna.analysis.feature_relevance.relevance_table import get_model_relevance_table
 from etna.analysis.feature_relevance.relevance_table import get_statistics_relevance_table
@@ -21,8 +22,15 @@ def __init__(self, greater_is_better: bool):
         """
         self.greater_is_better = greater_is_better
 
+    def _get_ranks(self, table: pd.DataFrame) -> pd.DataFrame:
+        """Compute rank relevance table from relevance table."""
+        if self.greater_is_better:
+            table *= -1
+        rank_table = pd.DataFrame(scipy.stats.rankdata(table, axis=1), columns=table.columns, index=table.index)
+        return rank_table.astype(int)
+
     @abstractmethod
-    def __call__(self, df: pd.DataFrame, df_exog: pd.DataFrame, **kwargs) -> pd.DataFrame:
+    def __call__(self, df: pd.DataFrame, df_exog: pd.DataFrame, return_ranks: bool = False, **kwargs) -> pd.DataFrame:
         """Compute relevance table.
         For each series in df compute relevance of corresponding series in df_exog.
 
@@ -32,6 +40,8 @@ def __call__(self, df: pd.DataFrame, df_exog: pd.DataFrame, **kwargs) -> pd.Data
             dataframe with series that will be used as target
         df_exog:
             dataframe with series to compute relevance for df
+        return_ranks:
+            if False return relevance values else return ranks of relevance values
 
         Returns
         -------
@@ -47,9 +57,11 @@ class StatisticsRelevanceTable(RelevanceTable):
     def __init__(self):
         super().__init__(greater_is_better=False)
 
-    def __call__(self, df: pd.DataFrame, df_exog: pd.DataFrame, **kwargs) -> pd.DataFrame:
+    def __call__(self, df: pd.DataFrame, df_exog: pd.DataFrame, return_ranks: bool = False, **kwargs) -> pd.DataFrame:
         """Compute feature relevance table with etna.analysis.get_statistics_relevance_table method."""
         table = get_statistics_relevance_table(df=df, df_exog=df_exog)
+        if return_ranks:
+            return self._get_ranks(table)
         return table
 
 
@@ -59,7 +71,9 @@ class ModelRelevanceTable(RelevanceTable):
     def __init__(self):
         super().__init__(greater_is_better=True)
 
-    def __call__(self, df: pd.DataFrame, df_exog: pd.DataFrame, **kwargs) -> pd.DataFrame:
+    def __call__(self, df: pd.DataFrame, df_exog: pd.DataFrame, return_ranks: bool = False, **kwargs) -> pd.DataFrame:
         """Compute feature relevance table with etna.analysis.get_model_relevance_table method."""
         table = get_model_relevance_table(df=df, df_exog=df_exog, **kwargs)
+        if return_ranks:
+            return self._get_ranks(table)
         return table
diff --git a/tests/test_analysis/test_feature_relevance/test_relevance.py b/tests/test_analysis/test_feature_relevance/test_relevance.py
index 0fa11b9cc..100be660d 100644
--- a/tests/test_analysis/test_feature_relevance/test_relevance.py
+++ b/tests/test_analysis/test_feature_relevance/test_relevance.py
@@ -1,3 +1,4 @@
+import pytest
 from sklearn.tree import DecisionTreeRegressor
 
 from etna.analysis.feature_relevance import ModelRelevanceTable
@@ -8,11 +9,26 @@ def test_statistics_relevance_table(simple_df_relevance):
     rt = StatisticsRelevanceTable()
     assert not rt.greater_is_better
     df, df_exog = simple_df_relevance
-    assert rt(df=df, df_exog=df_exog).shape == (2, 2)
+    assert rt(df=df, df_exog=df_exog, return_ranks=False).shape == (2, 2)
 
 
 def test_model_relevance_table(simple_df_relevance):
     rt = ModelRelevanceTable()
     assert rt.greater_is_better
     df, df_exog = simple_df_relevance
-    assert rt(df=df, df_exog=df_exog, model=DecisionTreeRegressor()).shape == (2, 2)
+    assert rt(df=df, df_exog=df_exog, return_ranks=False, model=DecisionTreeRegressor()).shape == (2, 2)
+
+
+@pytest.mark.parametrize(
+    "greater_is_better,answer",
+    ((True, [1, 2, 2, 1]), (False, [2, 1, 1, 2])),
+)
+def test_relevance_table_ranks(greater_is_better, answer, simple_df_relevance):
+    rt = ModelRelevanceTable()
+    rt.greater_is_better = greater_is_better
+    df, df_exog = simple_df_relevance
+    table = rt(df=df, df_exog=df_exog, return_ranks=True, model=DecisionTreeRegressor())
+    assert table["regressor_1"]["1"] == answer[0]
+    assert table["regressor_2"]["1"] == answer[1]
+    assert table["regressor_1"]["2"] == answer[2]
+    assert table["regressor_2"]["2"] == answer[3]