feat: add PEMS and Solar dataset support

- Add Dataset_PEMS and Dataset_Solar classes for PEMS and Solar datasets
- Update data_factory.py to include new dataset mappings
- Fix M4 dataset handling with proper numpy array dtype
- Add PEMS-specific loss function (L1Loss) and inverse transform support
- Update validation logic for PEMS dataset with inverse scaling
- Fix M4 data loader insample mask calculation bug

Changes support new traffic and solar energy datasets while maintaining
backward compatibility with existing datasets.

exp/exp_long_term_forecasting.py

@@ -34,9 +34,19 @@ class Exp_Long_Term_Forecast(Exp_Basic):
         model_optim = optim.Adam(self.model.parameters(), lr=self.args.learning_rate)
         return model_optim
 
-    def _select_criterion(self):
-        criterion = nn.MSELoss()
-        return criterion
+    def _select_criterion(self, loss_name='MSE'):
+        if self.args.data == 'PEMS':
+            return nn.L1Loss()
+        elif loss_name == 'MSE':
+            return nn.MSELoss()
+        elif loss_name == 'MAPE':
+            return mape_loss()
+        elif loss_name == 'MASE':
+            return mase_loss()
+        elif loss_name == 'SMAPE':
+            return smape_loss()
+        elif loss_name == 'MAE':
+            return nn.L1Loss(reduction='mean')
  
 
     def vali(self, vali_data, vali_loader, criterion):
@@ -66,9 +76,18 @@ class Exp_Long_Term_Forecast(Exp_Basic):
                 pred = outputs.detach()
                 true = batch_y.detach()
 
-                loss = criterion(pred, true)
+                if self.args.data == 'PEMS':
+                    B, T, C = pred.shape
+                    pred = pred.cpu().numpy()
+                    true = true.cpu().numpy()
+                    pred = vali_data.inverse_transform(pred.reshape(-1, C)).reshape(B, T, C)
+                    true = vali_data.inverse_transform(true.reshape(-1, C)).reshape(B, T, C)
+                    mae, mse, rmse, mape, mspe = metric(pred, true)
+                    total_loss.append(mae)
+                else:
+                    loss = criterion(pred, true)
+                    total_loss.append(loss.item())
 
-                total_loss.append(loss.item())
         total_loss = np.average(total_loss)
         self.model.train()
         return total_loss