diff --git a/pytorch_forecasting/data/timeseries/_timeseries.py b/pytorch_forecasting/data/timeseries/_timeseries.py
index 043a51d1e..4895cdecb 100644
--- a/pytorch_forecasting/data/timeseries/_timeseries.py
+++ b/pytorch_forecasting/data/timeseries/_timeseries.py
@@ -1860,7 +1860,7 @@ def _construct_index(self, data: pd.DataFrame, predict_mode: bool) -> pd.DataFra
         )
         assert len(df_index) > 0, msg
 
-        return df_index
+        return df_index.to_records(index=True)
 
     def filter(self, filter_func: Callable, copy: bool = True) -> TimeSeriesDataType:
         """Filter subsequences in dataset.
@@ -1909,8 +1909,8 @@ def decoded_index(self) -> pd.DataFrame:
             pd.DataFrame: index that can be understood in terms of original data
         """
         # get dataframe to filter
-        index_start = self.index["index_start"].to_numpy()
-        index_last = self.index["index_end"].to_numpy()
+        index_start = self.index["index_start"]
+        index_last = self.index["index_end"]
         index = (
             # get group ids in order of index
             pd.DataFrame(
@@ -2091,16 +2091,20 @@ def __getitem__(self, idx: int) -> tuple[dict[str, torch.Tensor], torch.Tensor]:
         Returns:
             tuple[dict[str, torch.Tensor], torch.Tensor]: x and y for model
         """
-        index = self.index.iloc[idx]
+        index = self.index[idx]
 
+        # get index data
+        index_start = index.index_start
+        index_end = index.index_end
+        index_sequence_length = index.sequence_length
         # slice data based on index
-        idx_slice = slice(index.index_start, index.index_end + 1)
+        idx_slice = slice(index_start, index_end + 1)
 
         data_cont = self.data["reals"][idx_slice].clone()
         data_cat = self.data["categoricals"][idx_slice].clone()
         time = self.data["time"][idx_slice].clone()
         target = [d[idx_slice].clone() for d in self.data["target"]]
-        groups = self.data["groups"][index.index_start].clone()
+        groups = self.data["groups"][index_start].clone()
         if self.data["weight"] is None:
             weight = None
         else:
@@ -2112,7 +2116,7 @@ def __getitem__(self, idx: int) -> tuple[dict[str, torch.Tensor], torch.Tensor]:
 
         # fill in missing values (if not all time indices are specified)
         sequence_length = len(time)
-        if sequence_length < index.sequence_length:
+        if sequence_length < index_sequence_length:
             assert (
                 self.allow_missing_timesteps
             ), "allow_missing_timesteps should be True if sequences have gaps"