diff --git a/seq2seq_time/data/dataset.py b/seq2seq_time/data/dataset.py
index d344917..1550dca 100644
--- a/seq2seq_time/data/dataset.py
+++ b/seq2seq_time/data/dataset.py
@@ -41,13 +41,15 @@ class Seq2SeqDataSet(torch.utils.data.Dataset):
         self.columns_past = columns_past
 
         # For speed
-        self._icol_blank = [df.drop(columns = columns_target).columns.tolist().index(n) for n in columns_past]
+        self._icol_blank = [df.drop(columns = columns_target).columns.tolist().index(n) for n in columns_past] # the columns to blank for future x
         self._x = self.df.drop(columns = self.columns_target).values
         self._y = self.df[columns_target].values
         
         # Sometimes we want to have it shuffled, but the same each time
         np.random.seed(42)
         self._rand_index = np.random.permutation(len(self))
+        
+        self.index = self.df.index[self.window_past:-1]
 
     def get_components(self, i):
         """Get past and future rows."""
@@ -70,7 +72,7 @@ class Seq2SeqDataSet(torch.utils.data.Dataset):
         y_future = y[self.window_past:]
 
         # Stop it cheating by using future weather measurements. Fill in with last value
-        x_future[:, self._icol_blank] = x_past[0, self._icol_blank]
+        x_future[:, self._icol_blank] = x_past[-1, self._icol_blank]
 
         # x_future[:, self._icol_blank] = 0
         return x_past, y_past, x_future, y_future
@@ -113,7 +115,7 @@ class Seq2SeqDataSet(torch.utils.data.Dataset):
     
     def __repr__(self):
         t = self.df.index
-        return f'<{type(self).__name__}(shape={self.df.shape}, times={t[0]} to {t[1]})>'
+        return f'<{type(self).__name__}(shape={self.df.shape}, times={t[0]} to {t[-1]})>'
 
 
 class Seq2SeqDataSets(torch.utils.data.Dataset):