diff --git a/src/gluonts/model/r_forecast/_predictor.py b/src/gluonts/model/r_forecast/_predictor.py
index dd385b5a24..50ce2c4b7a 100644
--- a/src/gluonts/model/r_forecast/_predictor.py
+++ b/src/gluonts/model/r_forecast/_predictor.py
@@ -249,6 +249,8 @@ def predict(
 
         for data in dataset:
             if self.trunc_length:
+                shift_by = max(data["target"].shape[0] - self.trunc_length, 0)
+                data["start"] = data["start"] + shift_by
                 data["target"] = data["target"][-self.trunc_length :]
 
             params = self.params.copy()
diff --git a/test/model/r_forecast/test_r_predictor.py b/test/model/r_forecast/test_r_predictor.py
index 49879f9622..6af26013b8 100644
--- a/test/model/r_forecast/test_r_predictor.py
+++ b/test/model/r_forecast/test_r_predictor.py
@@ -15,7 +15,7 @@
 
 from gluonts.core import serde
 from gluonts.dataset.repository import datasets
-from gluonts.dataset.util import forecast_start
+from gluonts.dataset.util import forecast_start, to_pandas
 from gluonts.evaluation import Evaluator, backtest_metrics
 from gluonts.model.forecast import SampleForecast, QuantileForecast
 from gluonts.model.r_forecast import (
@@ -98,6 +98,16 @@ def test_forecasts(method_name):
     assert agg_metrics["NRMSE"] < TOLERANCE
     assert agg_metrics["RMSE"] < TOLERANCE
 
+    trunc_length = prediction_length
+
+    predictor = RForecastPredictor(**params, trunc_length=trunc_length)
+    predictions = list(predictor.predict(train_dataset))
+
+    assert all(
+        prediction.start_date == to_pandas(data).index[-1] + 1
+        for data, prediction in zip(train_dataset, predictions)
+    )
+
 
 def test_r_predictor_serialization():
     predictor = RForecastPredictor(freq="1D", prediction_length=3)