use history_length - context_length for past_time_feat in deepvar

pts/model/deepvar/deepvar_network.py

@@ -238,13 +238,18 @@ class DeepVARTrainingNetwork(nn.Module):
         )
 
         if future_time_feat is None or future_target_cdf is None:
-            time_feat = past_time_feat[:, -self.context_length :, ...]
+            time_feat = past_time_feat[
+                :, self.history_length - self.context_length :, ...
+            ]
             sequence = past_target_cdf
             sequence_length = self.history_length
             subsequences_length = self.context_length
         else:
             time_feat = torch.cat(
-                (past_time_feat[:, -self.context_length :, ...], future_time_feat),
+                (
+                    past_time_feat[:, self.history_length - self.context_length :, ...],
+                    future_time_feat,
+                ),
                 dim=1,
             )
             sequence = torch.cat((past_target_cdf, future_target_cdf), dim=1)
@@ -401,7 +406,7 @@ class DeepVARTrainingNetwork(nn.Module):
 
         # mask the loss at one time step if one or more observations is missing
         # in the target dimensions (batch_size, subseq_length, 1)
-        loss_weights,_ = observed_values.min(dim=-1, keepdim=True)
+        loss_weights, _ = observed_values.min(dim=-1, keepdim=True)
 
         # assert_shape(loss_weights, (-1, seq_len, 1))
 
@@ -430,7 +435,7 @@ class DeepVARPredictionNetwork(DeepVARTrainingNetwork):
         target_dimension_indicator: torch.Tensor,
         time_feat: torch.Tensor,
         scale: torch.Tensor,
-        begin_states: Union[List[torch.Tensor], torch.Tensor]
+        begin_states: Union[List[torch.Tensor], torch.Tensor],
     ) -> torch.Tensor:
         """
         Computes sample paths by unrolling the RNN starting with a initial