diff --git a/examples/metaLearing_exp.py b/examples/metaLearing_exp.py
new file mode 100644
index 0000000..c1b7d7a
--- /dev/null
+++ b/examples/metaLearing_exp.py
@@ -0,0 +1,130 @@
+import sys
+import os
+sys.path.insert(0, os.getcwd())
+
+def main():
+    """ - First, we create synthetic dataset.
+        - We add meta-learning pipeline.
+        - Then, we extract meta-features of time series with three
+            deep learning methods, i.e., EDMLP, EDLSTM, EDTCN.
+        - We also extract statistical features of time series with TsFresh.
+        - Then, We add 2 base_forecasters
+        - Finally, we fit and predict time series.    
+    """
+
+    ##########
+    # dataset
+    ##########    
+    
+    LEN_TS = 39 # time series length
+    FH = 7      # forecasting horizon
+    
+    # create synthetic data    
+    from metats.datasets import ETSDataset
+    ets_generator = ETSDataset({'A,N,N': 512,
+                                'M,M,M': 512}, length=LEN_TS, freq=4)
+    data, labels = ets_generator.load(return_family=True)
+    colors = list(map(lambda x: (x=='A,N,N')*1, labels))
+    
+    # scaling data
+    from sklearn.preprocessing import StandardScaler
+    scaled_data = StandardScaler().fit_transform(data.T)
+    data = scaled_data.T[:, :, None]    # size: batch_dim x seires_length x series_dim
+    print('data shape is:',data.shape)
+    
+    # creats MetaLearning pipeline
+    from metats.pipeline import MetaLearning
+    pipeline = MetaLearning(method='selection', loss='mse')
+    
+    ##########
+    # MLP
+    ##########
+    
+    from metats.features.unsupervised import DeepAutoEncoder
+    from metats.features.deep import AutoEncoder, MLPEncoder, MLPDecoder
+
+    enc = MLPEncoder(input_size=data.shape[2], input_length=LEN_TS-FH, latent_size=8, hidden_layers=(16,))
+    dec = MLPDecoder(input_size=data.shape[2], input_length=LEN_TS-FH, latent_size=8, hidden_layers=(16,))
+    ae = AutoEncoder(encoder=enc, decoder=dec)
+    ae_features = DeepAutoEncoder(auto_encoder=ae, epochs=100, verbose=False)
+    
+    pipeline.add_feature(ae_features)
+    
+    ##########
+    # LSTM
+    ##########
+    
+    from metats.features.unsupervised import DeepAutoEncoder
+    from metats.features.deep import AutoEncoder, LSTMDecoder, LSTMEncoder
+
+    H = 5   # hidden size
+    l = 3   # latent size
+    NL= 2   # number of layers
+
+    enc = LSTMEncoder(input_size=data.shape[2], latent_size=l,
+                    hidden_size=H, num_layers=NL, directions=1)
+    dec = LSTMDecoder(output_length=LEN_TS-FH, output_size=data.shape[2],
+                    latent_size=l, hidden_size=H, num_layers=NL, directions=1)
+    ae = AutoEncoder(encoder=enc, decoder=dec)
+    ae_features = DeepAutoEncoder(auto_encoder=ae, epochs=100, verbose=False)
+    pipeline.add_feature(ae_features)
+
+    ##########
+    # TCN
+    ##########
+
+    from metats.features.unsupervised import DeepAutoEncoder
+    from metats.features.deep import AutoEncoder, Encoder_Decoder_TCN
+    
+    # construct model
+    EDTCN = Encoder_Decoder_TCN(input_size=data.shape[2], input_length=LEN_TS-FH,             
+                                hidden_layers=(4,1), kernel_size =4, dilation=2)
+    enc = EDTCN.encoder
+    dec = EDTCN.decoder
+    ae = AutoEncoder(encoder=enc, decoder=dec)
+    ae_features = DeepAutoEncoder(auto_encoder=ae, epochs=100, verbose=True)
+    
+    pipeline.add_feature(ae_features)
+    
+    ##########
+    # TsFresh
+    ##########
+
+    from metats.features.statistical import TsFresh  
+
+    # adding TsFresh as statistical features extractor
+    stat_features = TsFresh()
+    pipeline.add_feature(stat_features)
+    
+    ##########
+    # Forecasters
+    ##########
+    
+    from sktime.forecasting.naive import NaiveForecaster
+    from sktime.forecasting.compose import make_reduction
+    from sklearn.neighbors import KNeighborsRegressor
+    
+    # creating two base-forecasters
+    regressor = KNeighborsRegressor(n_neighbors=1)
+    forecaster1 = make_reduction(regressor, window_length=15, strategy="recursive")
+    forecaster2 = NaiveForecaster() 
+    
+    # adding base-forecasters to pipline
+    pipeline.add_forecaster(forecaster1)
+    pipeline.add_forecaster(forecaster2)
+    
+    ##########
+    # MetaLearner
+    ##########
+
+    from sklearn.ensemble import RandomForestClassifier
+    # to adopt random foreset as meta-learner:
+    # meta-learner makes connection between features and 
+    # each base-forecaster's weights 
+    pipeline.add_metalearner(RandomForestClassifier())
+    pipeline.fit(data, fh=FH)
+    predict = pipeline.predict(data, fh=FH)
+    print(predict.shape)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/examples/readme.md b/examples/readme.md
new file mode 100644
index 0000000..ad7a80a
--- /dev/null
+++ b/examples/readme.md
@@ -0,0 +1,12 @@
+`metaLearning_exp.py` is a simple example that shows how we can integrate three deep-learning feature extraction methods and one statistical feature extractor, to perform model selction between two base-forecasters.
+
+You can run:
+``` bash
+python examples\metaLearing_exp.py
+```
+---
+**NOTE:**
+
+Good accuracy usually requires precise Hyper-parmeters selection of NN's architectures, etc.
+
+---
\ No newline at end of file
diff --git a/metats/features/deep.py b/metats/features/deep.py
index 4080899..c496371 100644
--- a/metats/features/deep.py
+++ b/metats/features/deep.py
@@ -89,8 +89,8 @@ def forward(self, latent):
     h0, c0 = self.get_initial(bsize)
 
     out, _ = self.lstm(lstm_in, (h0, c0))
-    Y = 0.5 * (out[:, :, :self.output_size] + out[:, :, self.output_size:])
-    Y = Y.permute(1, 0, 2)
+    # Y = 0.5 * (out[:, :, :self.output_size] + out[:, :, self.output_size:])
+    Y = out.permute(1, 0, 2)
     return Y
 
 
@@ -271,7 +271,7 @@ def forward(self, latent):
     return Y
 
 
-class Encoder_Decoder_TCN(nn.Module):
+class Encoder_Decoder_TCN():
   """
   A general class for Encoder decoder with 
   dilated Temporal Convolutional Networks (TCN).
@@ -296,37 +296,63 @@ def __init__(self, input_size, input_length, hidden_layers=(128,64),
       raise ValueError(f"'Time series length' has to be divisible by number of "\
                        f"'hidden_layers', but {input_length} is not divisible by {2**len(hidden_layers)}!")
     
-    super().__init__()
     depth = len(hidden_layers)
     if activation == None:
       activation = nn.Tanh
 
-    
     ##    Encoder: 
-    model = []
-    for i in range(depth):
-      dilation_size = 2 ** i
-      in_channels = input_size if i == 0 else hidden_layers[i-1]
-      model.append(nn.Conv1d(in_channels, hidden_layers[i],
-                             kernel_size, padding='same', dilation=dilation_size))
-      model.append(nn.Dropout(dropout))
-      model.append(activation())
-      model.append(nn.MaxPool1d(2))  
-    self.encoder = nn.Sequential(*model)
-    self.encoder.latent_size = hidden_layers[-1]
-
-    ##    Decoder:
-    model = []
-    for i in range(depth-1,-1,-1):
-      dilation_size = 2 ** i
-      out_channels = input_size if i == 0 else hidden_layers[i-1]
-      model.append(nn.Upsample(scale_factor=2))
-      model.append(nn.Conv1d(hidden_layers[i], out_channels,
-                             kernel_size=kernel_size, padding='same', dilation=dilation_size))
-      model.append(nn.Dropout(dropout))
-      model.append(activation())
-
-    self.decoder = nn.Sequential(*model)
+    class Encoder(nn.Module):
+      def __init__(self):
+        super().__init__()
+        model = []
+        for i in range(depth):
+          dilation_size = 2 ** i
+          in_channels = input_size if i == 0 else hidden_layers[i-1]
+          model.append(nn.Conv1d(in_channels, hidden_layers[i],
+                                kernel_size, padding='same', dilation=dilation_size))
+          model.append(nn.Dropout(dropout))
+          model.append(activation())
+          model.append(nn.MaxPool1d(2))  
+        self._encoder = nn.Sequential(*model)
+        self.latent_size = self._encoder_dim()
+      
+      def _encoder_dim(self):
+        """gets endoder laten dimension
+
+        Returns:
+            int: size of the latant dimension
+        """
+        x=torch.randn(1, input_size, input_length)
+        encode = self._encoder(x).reshape(1,-1) 
+        return encode.shape[1]
+      
+      def forward(self,x):
+        x = x.permute(0, 2, 1)
+        y = self._encoder(x)
+        return torch.squeeze(y,dim=1)    
+    
+    ##    Decoder:  
+    class Decoder(nn.Module):
+      def __init__(self):      
+        super().__init__()
+        model = []
+        for i in range(depth-1,-1,-1):
+          dilation_size = 2 ** i
+          out_channels = input_size if i == 0 else hidden_layers[i-1]
+          model.append(nn.Upsample(scale_factor=2))
+          model.append(nn.Conv1d(hidden_layers[i], out_channels,
+                                kernel_size=kernel_size, padding='same', dilation=dilation_size))
+          model.append(nn.Dropout(dropout))
+          model.append(activation())
+        self._decoder = nn.Sequential(*model)
+      
+      def forward(self,x):
+        x = torch.unsqueeze(x,dim=1)
+        y = self._decoder(x)
+        return y        
+  
+    self.encoder = Encoder()
+    self.decoder = Decoder()
 
 class AutoEncoder(nn.Module):
   """