diff --git a/qlib/contrib/model/pytorch_alstm_ts.py b/qlib/contrib/model/pytorch_alstm_ts.py
index 008d789402..3fb7cb9e19 100644
--- a/qlib/contrib/model/pytorch_alstm_ts.py
+++ b/qlib/contrib/model/pytorch_alstm_ts.py
@@ -160,6 +160,10 @@ def metric_fn(self, pred, label):
 
         if self.metric in ("", "loss"):
             return -self.loss_fn(pred[mask], label[mask])
+        elif self.metric == "mse":
+            mask = ~torch.isnan(label)
+            weight = torch.ones_like(label)
+            return -self.mse(pred[mask], label[mask], weight[mask])
 
         raise ValueError("unknown metric `%s`" % self.metric)
 
diff --git a/qlib/data/data.py b/qlib/data/data.py
index 1b1353ee4e..aba75c0b1a 100644
--- a/qlib/data/data.py
+++ b/qlib/data/data.py
@@ -616,7 +616,7 @@ def inst_calculator(inst, start_time, end_time, freq, column_names, spans=None,
 
         data = pd.DataFrame(obj)
         if not data.empty and not np.issubdtype(data.index.dtype, np.dtype("M")):
-            # If the underlaying provides the data not in datatime formmat, we'll convert it into datetime format
+            # If the underlaying provides the data not in datetime format, we'll convert it into datetime format
             _calendar = Cal.calendar(freq=freq)
             data.index = _calendar[data.index.values.astype(int)]
         data.index.names = ["datetime"]
diff --git a/qlib/workflow/task/utils.py b/qlib/workflow/task/utils.py
index 19837b3c79..4b4a7c06b8 100644
--- a/qlib/workflow/task/utils.py
+++ b/qlib/workflow/task/utils.py
@@ -242,7 +242,7 @@ def _add_step(self, index, step):
 
     def shift(self, seg: tuple, step: int, rtype=SHIFT_SD) -> tuple:
         """
-        Shift the datatime of segment
+        Shift the datetime of segment
 
         If there are None (which indicates unbounded index) in the segment, this method will return None.