diff --git a/darts/explainability/shap_explainer.py b/darts/explainability/shap_explainer.py
index 143ea0d8b9..b45fb275cf 100644
--- a/darts/explainability/shap_explainer.py
+++ b/darts/explainability/shap_explainer.py
@@ -25,7 +25,7 @@
 """
 
 from enum import Enum
-from typing import Dict, NewType, Optional, Sequence, Union
+from typing import Dict, List, NewType, Optional, Sequence, Union
 
 import matplotlib.pyplot as plt
 import pandas as pd
@@ -375,7 +375,7 @@ def summary_plot(
         num_samples: Optional[int] = None,
         plot_type: Optional[str] = "dot",
         **kwargs,
-    ):
+    ) -> List[shap.Explanation]:
         """
         Display a shap plot summary for each horizon and each component dimension of the target.
         This method reuses the initial background data as foreground (potentially sampled) to give a general importance
@@ -395,6 +395,11 @@ def summary_plot(
             for the sake of performance.
         plot_type
             Optionally, specify which of the shap library plot type to use. Can be one of ``'dot', 'bar', 'violin'``.
+
+        Returns
+        -------
+        shap_explanations
+            A list containing the raw Explanations of the visualized the horizons and components
         """
 
         horizons, target_components = self._process_horizons_and_targets(
@@ -412,8 +417,10 @@ def summary_plot(
             foreground_X_sampled, horizons, target_components
         )
 
+        shap_explanations = []
         for t in target_components:
             for h in horizons:
+                shap_explanations.append(shaps_[h][t])
                 plt.title("Target: `{}` - Horizon: {}".format(t, "t+" + str(h)))
                 shap.summary_plot(
                     shaps_[h][t],
@@ -422,6 +429,8 @@ def summary_plot(
                     **kwargs,
                 )
 
+        return shap_explanations
+
     def force_plot_from_ts(
         self,
         foreground_series: Optional[TimeSeries] = None,
@@ -613,7 +622,7 @@ def __init__(
 
     def shap_explanations(
         self,
-        foreground_X,
+        foreground_X: pd.DataFrame,
         horizons: Optional[Sequence[int]] = None,
         target_components: Optional[Sequence[str]] = None,
     ) -> Dict[int, Dict[str, shap.Explanation]]:
@@ -735,8 +744,8 @@ def _create_regression_model_shap_X(
         target_series: Optional[Union[TimeSeries, Sequence[TimeSeries]]],
         past_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]],
         future_covariates: Optional[Union[TimeSeries, Sequence[TimeSeries]]],
-        n_samples=None,
-        train=False,
+        n_samples: Optional[int] = None,
+        train: bool = False,
     ) -> pd.DataFrame:
         """
         Creates the shap format input for regression models.