diff --git a/pyFTS/common/Transformations.py b/pyFTS/common/Transformations.py
index 205251b..610f375 100644
--- a/pyFTS/common/Transformations.py
+++ b/pyFTS/common/Transformations.py
@@ -13,6 +13,7 @@ from pyFTS.common.transformations.boxcox import BoxCox
 from pyFTS.common.transformations.roi import ROI
 from pyFTS.common.transformations.trend import LinearTrend
 from pyFTS.common.transformations.som import SOMTransformation
+from pyFTS.common.transformations.autoencoder import AutoencoderTransformation
 from pyFTS.common.transformations.normalization import Normalization
 
 
diff --git a/pyFTS/common/transformations/autoencoder.py b/pyFTS/common/transformations/autoencoder.py
new file mode 100644
index 0000000..d1ec1ad
--- /dev/null
+++ b/pyFTS/common/transformations/autoencoder.py
@@ -0,0 +1,126 @@
+"""
+Autoencoders for Fuzzy Time Series
+"""
+
+import pandas as pd
+import numpy as np
+from keras.models import Model
+from keras.layers import Dense, Input
+from keras import regularizers
+from sklearn.preprocessing import MinMaxScaler
+from pyFTS.common.transformations.transformation import Transformation
+
+
+class AutoencoderTransformation(Transformation):
+    def __init__(self,
+                 reduced_dimension:int = 2,
+                 **kwargs):
+        
+        
+        
+        # Autoencoder attributes
+        self.load_file = kwargs.get('loadFile')
+        self.data: pd.DataFrame = None
+
+        self.is_multivariate = True
+        self.reduced_dimension = reduced_dimension
+        self.encoder_layers = []
+        self.decoder_layers = []
+        self.input = None
+        self.scaler = MinMaxScaler()
+
+
+        # debug attributes
+        self.name = 'Autoencoders FTS'
+        self.shortname = 'Autoencoders-FTS'
+    
+    
+    
+    
+    def apply(self,
+              data: pd.DataFrame,
+              param=None,
+              **kwargs):
+        """
+        Transform a N-dimensional dataset into a n-dimensional dataset, where one dimension is the endogen variable
+        If endogen_variable = None, the last column will be the endogen_variable.
+        Args:
+            data (pd.DataFrame): N-Dimensional dataset
+            endogen_variable (str):  column of dataset
+            names (Tuple): names for new columns created by the AutoEncoders Transformation.
+            param:
+            **kwargs: params of AE's train process
+                percentage_train (float). Percentage of dataset that will be used for train SOM network. default: 0.7
+                epochs: epochs of SOM network. default: 10000
+
+        """
+
+        endogen_variable = kwargs.get('endogen_variable', None)
+        names = kwargs.get('names', ('x', 'y'))
+
+        if endogen_variable not in data.columns:
+            endogen_variable = None
+        
+        encoder = Model(inputs=self.input, outputs=self.encoder_layers[-1])
+        
+        cols = data.columns[:-1] if endogen_variable is None else [col for col in data.columns if
+                                                                   col != endogen_variable]
+        
+        data_scaled = self.scaler.fit_transform(data[cols])
+
+        new_data = pd.DataFrame(encoder.predict(data_scaled), columns = names)
+        
+        endogen = endogen_variable if endogen_variable is not None else data.columns[-1]
+        new_data[endogen] = data[endogen].values
+        return new_data
+    
+    
+        
+        
+        
+        
+        
+    def train(self,
+                  data: pd.DataFrame,
+                  percentage_train: float = .7,    #usar todos os dados ou só o treino para treinar a RNA?
+                  epochs: int = 100,
+                  n_layers: int = 2,
+                  neuron_per_layer: list = []):
+
+        self.encoder_layers.clear()
+        self.decoder_layers.clear()
+        data = data.dropna() 
+        self.data = data.values
+        limit = round(len(self.data) * percentage_train)
+        train = self.data[:limit]
+        counter = 0
+        if (n_layers==1):
+            multi_layer = False
+        else:
+            multi_layer = True
+                
+        data_scaled = self.scaler.fit_transform(data)
+        if (neuron_per_layer == []):
+            n = data_scaled.shape[1] - self.reduced_dimension
+            aux = (n/n_layers)
+            for i in range (1, n_layers):
+                neuron_per_layer.append(data_scaled.shape[1] - round(aux*i))
+                
+        self.input = Input(shape=(data_scaled.shape[1], ))     
+        if (multi_layer):
+            self.encoder_layers.append(Dense(neuron_per_layer[0], activation="tanh", activity_regularizer=regularizers.l1(10e-5))(self.input))
+            for i in range (1, n_layers-1):
+                self.encoder_layers.append(Dense(neuron_per_layer[i], activation="tanh")(self.encoder_layers[i-1]))
+            self.encoder_layers.append(Dense(self.reduced_dimension, activation="tanh")(self.encoder_layers[-1]))
+            self.decoder_layers.append(Dense(neuron_per_layer[-1], activation="tanh", activity_regularizer=regularizers.l1(10e-5))(self.encoder_layers[-1]))
+            for i in range (n_layers-3, -1, -1):    
+                self.decoder_layers.append(Dense(neuron_per_layer[i], activation="tanh")(self.decoder_layers[counter]))
+                counter+=1
+            self.decoder_layers.append(Dense(data_scaled.shape[1], activation="tanh")(self.decoder_layers[counter]))
+        else:
+            self.encoder_layers.append(Dense(self.reduced_dimension, activation="tanh", activity_regularizer=regularizers.l1(10e-5))(self.input))
+            self.decoder_layers.append(Dense(data_scaled.shape[1], activation="tanh", activity_regularizer=regularizers.l1(10e-5))(self.encoder_layers[0]))
+        autoencoder = Model(self.input, self.decoder_layers[-1])
+        autoencoder.compile(optimizer = 'adam', loss='mse')
+        X_train = data_scaled
+        autoencoder.fit(x=X_train, y=X_train, epochs=epochs)
\ No newline at end of file