diff --git a/lifelines/fitters/cox_time_varying_fitter.py b/lifelines/fitters/cox_time_varying_fitter.py
index d58cb4112..db0e1cb0f 100644
--- a/lifelines/fitters/cox_time_varying_fitter.py
+++ b/lifelines/fitters/cox_time_varying_fitter.py
@@ -295,6 +295,15 @@ def _compute_confidence_intervals(self):
             index=self.params_.index,
         )
 
+    def calculate_survival_function(self, X: pd.DataFrame) -> pd.Series:
+        v = self.predict_partial_hazard(X)
+        col = utils._get_index(v)
+        times_ = X.start.values
+        c_0 = utils.interpolate_at_times(self.baseline_cumulative_hazard_, [times_]).T
+        cumulative_hazard_ = pd.DataFrame(c_0 * v.values, columns=col, index=times_)
+        surv_func = pd.Series(np.exp(-cumulative_hazard_).values.diagonal(), index=times_)
+        return surv_func
+        
     @property
     def summary(self):
         """Summary statistics describing the fit.
diff --git a/lifelines/tests/test_estimation.py b/lifelines/tests/test_estimation.py
index abf1d5f97..defbf3b50 100644
--- a/lifelines/tests/test_estimation.py
+++ b/lifelines/tests/test_estimation.py
@@ -5134,6 +5134,14 @@ def test_inference_against_known_R_output(self, ctv, dfcv):
         npt.assert_almost_equal(ctv.summary["se(coef)"].values, [1.229, 1.206], decimal=3)
         npt.assert_almost_equal(ctv.summary["p"].values, [0.14, 0.56], decimal=2)
 
+    def test_survival_function(self, ctv, dfcv):
+        sf_validation = [0.866, 0.866, 0.748, 0.866, 0.866, 0.277, 0.954,
+                         0.977, 0.902, 0.977, 0.977, 0.902, 0.977, 0.954]
+        ctv.fit(dfcv, id_col="id", start_col="start", stop_col="stop", event_col="event")
+        sf = ctv.calculate_survival_function(dfcv)
+        assert sf.index.values is dfcv.start.values
+        npt.assert_almost_equal(sf.values, sf_validation, decimal=2)
+
     def test_that_id_col_is_optional(self, dfcv):
 
         ctv_with_id = CoxTimeVaryingFitter().fit(dfcv, id_col="id", start_col="start", stop_col="stop", event_col="event")