diff --git a/darts/models/forecasting/forecasting_model.py b/darts/models/forecasting/forecasting_model.py
index cc7d04c949..67ae8d58d9 100644
--- a/darts/models/forecasting/forecasting_model.py
+++ b/darts/models/forecasting/forecasting_model.py
@@ -1052,11 +1052,10 @@ def backtest(
                     errors = errors[0]
                 backtest_list.append(errors)
             else:
-
                 errors = [
-                    [metric_f(series, f) for metric_f in metric]
+                    [metric_f(target_ts, f) for metric_f in metric]
                     if len(metric) > 1
-                    else metric[0](series, f)
+                    else metric[0](target_ts, f)
                     for f in forecasts[idx]
                 ]
 
diff --git a/darts/tests/models/forecasting/test_backtesting.py b/darts/tests/models/forecasting/test_backtesting.py
index b57781ffd4..56e2be42a1 100644
--- a/darts/tests/models/forecasting/test_backtesting.py
+++ b/darts/tests/models/forecasting/test_backtesting.py
@@ -7,9 +7,17 @@
 import pytest
 
 from darts import TimeSeries
+from darts.datasets import AirPassengersDataset, MonthlyMilkDataset
 from darts.logging import get_logger
 from darts.metrics import mape, r2_score
-from darts.models import ARIMA, FFT, ExponentialSmoothing, NaiveDrift, Theta
+from darts.models import (
+    ARIMA,
+    FFT,
+    ExponentialSmoothing,
+    NaiveDrift,
+    NaiveSeasonal,
+    Theta,
+)
 from darts.tests.base_test_class import DartsBaseTestClass
 from darts.utils.timeseries_generation import gaussian_timeseries as gt
 from darts.utils.timeseries_generation import linear_timeseries as lt
@@ -257,6 +265,25 @@ def test_backtest_forecasting(self):
             self.assertEqual(pred.width, 2)
             self.assertEqual(pred.end_time(), linear_series.end_time())
 
+    def test_backtest_multiple_series(self):
+        series = [AirPassengersDataset().load(), MonthlyMilkDataset().load()]
+        model = NaiveSeasonal(K=1)
+
+        error = model.backtest(
+            series,
+            train_length=30,
+            forecast_horizon=2,
+            stride=1,
+            retrain=True,
+            last_points_only=False,
+            verbose=False,
+        )
+
+        expected = [11.63104, 6.09458]
+        self.assertEqual(len(error), 2)
+        self.assertAlmostEqual(error[0], expected[0], places=4)
+        self.assertAlmostEqual(error[1], expected[1], places=4)
+
     @unittest.skipUnless(TORCH_AVAILABLE, "requires torch")
     def test_backtest_regression(self):
         np.random.seed(4)