diff --git a/.gitignore b/.gitignore
index 166fdb12..c6a2d897 100644
--- a/.gitignore
+++ b/.gitignore
@@ -373,4 +373,4 @@ DerivedData/
 
 # User created
 VERSION
-version.py
\ No newline at end of file
+version.py
diff --git a/examples/timeseries/tscwgan_example.py b/examples/timeseries/tscwgan_example.py
new file mode 100644
index 00000000..cb1bf1a7
--- /dev/null
+++ b/examples/timeseries/tscwgan_example.py
@@ -0,0 +1,59 @@
+from numpy import reshape
+
+from ydata_synthetic.preprocessing.timeseries import processed_stock
+from ydata_synthetic.synthesizers.timeseries import TSCWGAN
+from ydata_synthetic.synthesizers import ModelParameters, TrainParameters
+from ydata_synthetic.postprocessing.regular.inverse_preprocesser import inverse_transform
+
+model = TSCWGAN
+
+#Define the GAN and training parameters
+noise_dim = 32
+dim = 128
+seq_len = 48
+cond_dim = 24
+batch_size = 128
+
+log_step = 100
+epochs = 300+1
+learning_rate = 5e-4
+beta_1 = 0.5
+beta_2 = 0.9
+models_dir = './cache'
+critic_iter = 5
+
+# Get transformed data stock - Univariate
+data, processed_data, scaler = processed_stock(path='./data/stock_data.csv', seq_len=seq_len, cols = ['Open'])
+data_sample = processed_data[0]
+
+model_parameters = ModelParameters(batch_size=batch_size,
+                           lr=learning_rate,
+                           betas=(beta_1, beta_2),
+                           noise_dim=noise_dim,
+                           n_cols=seq_len,
+                           layers_dim=dim,
+                           condition = cond_dim)
+
+train_args = TrainParameters(epochs=epochs,
+                             sample_interval=log_step,
+                             critic_iter=critic_iter)
+
+#Training the TSCWGAN model
+synthesizer = model(model_parameters, gradient_penalty_weight=10)
+synthesizer.train(processed_data, train_args)
+
+#Saving the synthesizer to later generate new events
+synthesizer.save(path='./tscwgan_stock.pkl')
+
+#Loading the synthesizer
+synth = model.load(path='./tscwgan_stock.pkl')
+
+#Sampling the data
+#Note that the data returned is not inverse processed.
+cond_index = 100  # Arbitrary sequence for conditioning
+cond_array = reshape(processed_data[cond_index][:cond_dim], (1,-1))
+
+data_sample = synth.sample(cond_array, 1000, 100)
+
+# Inverting the scaling of the synthetic samples
+inv_data_sample = inverse_transform(data_sample, scaler)
diff --git a/src/ydata_synthetic/postprocessing/regular/inverse_preprocesser.py b/src/ydata_synthetic/postprocessing/regular/inverse_preprocesser.py
index 9b9a0b50..b99f4bc3 100644
--- a/src/ydata_synthetic/postprocessing/regular/inverse_preprocesser.py
+++ b/src/ydata_synthetic/postprocessing/regular/inverse_preprocesser.py
@@ -1,45 +1,46 @@
 # Inverts all preprocessing pipelines provided in the preprocessing examples
 from typing import Union
 
-import pandas as pd
+from pandas import DataFrame, concat
 
 from sklearn.pipeline import Pipeline
 from sklearn.compose import ColumnTransformer
-from sklearn.preprocessing import PowerTransformer, OneHotEncoder, StandardScaler
+from sklearn.preprocessing import PowerTransformer, OneHotEncoder, StandardScaler, MinMaxScaler
 
 
-def inverse_transform(data: pd.DataFrame, processor: Union[Pipeline, ColumnTransformer, PowerTransformer, OneHotEncoder, StandardScaler]) -> pd.DataFrame:
+def inverse_transform(data: DataFrame, processor: Union[Pipeline, ColumnTransformer, PowerTransformer,
+                                                           OneHotEncoder, StandardScaler, MinMaxScaler]) -> DataFrame:
     """Inverts data transformations taking place in a standard sklearn processor.
     Supported processes are sklearn pipelines, column transformers or base estimators like standard scalers.
 
     Args:
-        data (pd.DataFrame): The data object that needs inversion of preprocessing
+        data (DataFrame): The data object that needs inversion of preprocessing
         processor (Union[Pipeline, ColumnTransformer, BaseEstimator]): The processor applied on the original data
 
     Returns:
-        inv_data (pd.DataFrame): The data object after inverting preprocessing"""
+        inv_data (DataFrame): The data object after inverting preprocessing"""
     inv_data = data.copy()
-    if isinstance(processor, (PowerTransformer, OneHotEncoder, StandardScaler, Pipeline)):
-        inv_data = pd.DataFrame(processor.inverse_transform(data), columns=processor.feature_names_in_)
+    if isinstance(processor, (PowerTransformer, OneHotEncoder, StandardScaler, MinMaxScaler, Pipeline)):
+        inv_data = DataFrame(processor.inverse_transform(data), columns=processor.feature_names_in_ if hasattr(processor, "feature_names_in") else None)
     elif isinstance(processor, ColumnTransformer):
         output_indices = processor.output_indices_
-        assert isinstance(data, pd.DataFrame), "The data to be inverted from a ColumnTransformer has to be a Pandas DataFrame."
+        assert isinstance(data, DataFrame), "The data to be inverted from a ColumnTransformer has to be a Pandas DataFrame."
         for t_name, t, t_cols in processor.transformers_[::-1]:
             slice_ = output_indices[t_name]
             t_indices = list(range(slice_.start, slice_.stop, 1 if slice_.step is None else slice_.step))
             if t == 'drop':
                 continue
             elif t == 'passthrough':
-                inv_cols = pd.DataFrame(data.iloc[:,t_indices].values, columns = t_cols, index = data.index)
+                inv_cols = DataFrame(data.iloc[:,t_indices].values, columns = t_cols, index = data.index)
                 inv_col_names = inv_cols.columns
             else:
-                inv_cols = pd.DataFrame(t.inverse_transform(data.iloc[:,t_indices].values), columns = t_cols, index = data.index)
+                inv_cols = DataFrame(t.inverse_transform(data.iloc[:,t_indices].values), columns = t_cols, index = data.index)
                 inv_col_names = inv_cols.columns
             if set(inv_col_names).issubset(set(inv_data.columns)):
                 inv_data[inv_col_names] = inv_cols[inv_col_names]
             else:
-                inv_data = pd.concat([inv_data, inv_cols], axis=1)
+                inv_data = concat([inv_data, inv_cols], axis=1)
     else:
         print('The provided data processor is not supported and cannot be inverted with this method.')
         return None
-    return inv_data[processor.feature_names_in_]
+    return inv_data[processor.feature_names_in_] if hasattr(processor, "feature_names_in") else inv_data
diff --git a/src/ydata_synthetic/preprocessing/timeseries/stock.py b/src/ydata_synthetic/preprocessing/timeseries/stock.py
index f10367cc..26866adb 100644
--- a/src/ydata_synthetic/preprocessing/timeseries/stock.py
+++ b/src/ydata_synthetic/preprocessing/timeseries/stock.py
@@ -2,17 +2,30 @@
     Get the stock data from Yahoo finance data
     Data from the period 01 January 2017 - 24 January 2021
 """
+from typing import Optional, List
+
 import pandas as pd
+from typeguard import typechecked
 
 from ydata_synthetic.preprocessing.timeseries.utils import real_data_loading
 
-def transformations(path, seq_len: int):
-    stock_df = pd.read_csv(path)
+@typechecked
+def transformations(path, seq_len: int, cols: Optional[List] = None):
+    """Apply min max scaling and roll windows of a temporal dataset.
+
+    Args:
+        path(str): path to a csv temporal dataframe
+        seq_len(int): length of the rolled sequences
+        cols (Union[str, List]): Column or list of columns to be used"""
+    if isinstance(cols, list):
+        stock_df = pd.read_csv(path)[cols]
+    else:
+        stock_df = pd.read_csv(path)
     try:
         stock_df = stock_df.set_index('Date').sort_index()
     except:
         stock_df=stock_df
     #Data transformations to be applied prior to be used with the synthesizer model
-    processed_data = real_data_loading(stock_df.values, seq_len=seq_len)
+    data, processed_data, scaler = real_data_loading(stock_df.values, seq_len=seq_len)
 
-    return processed_data
+    return data, processed_data, scaler
diff --git a/src/ydata_synthetic/preprocessing/timeseries/utils.py b/src/ydata_synthetic/preprocessing/timeseries/utils.py
index c77c67b2..c8404899 100644
--- a/src/ydata_synthetic/preprocessing/timeseries/utils.py
+++ b/src/ydata_synthetic/preprocessing/timeseries/utils.py
@@ -4,7 +4,7 @@
 import numpy as np
 from sklearn.preprocessing import MinMaxScaler
 
-# Method implemented here: https://github.com/jsyoon0823/TimeGAN/blob/master/data_loading.py
+# Method adapted from here: https://github.com/jsyoon0823/TimeGAN/blob/master/data_loading.py
 # Originally used in TimeGAN research
 def real_data_loading(data: np.array, seq_len):
     """Load and preprocess real-world datasets.
@@ -30,7 +30,7 @@ def real_data_loading(data: np.array, seq_len):
 
     # Mix the datasets (to make it similar to i.i.d)
     idx = np.random.permutation(len(temp_data))
-    data = []
+    processed_data = []
     for i in range(len(temp_data)):
-        data.append(temp_data[idx[i]])
-    return data
+        processed_data.append(temp_data[idx[i]])
+    return data, processed_data, scaler
diff --git a/src/ydata_synthetic/synthesizers/gan.py b/src/ydata_synthetic/synthesizers/gan.py
index 6f7d5684..d1f658cc 100644
--- a/src/ydata_synthetic/synthesizers/gan.py
+++ b/src/ydata_synthetic/synthesizers/gan.py
@@ -21,10 +21,10 @@
 _model_parameters_df = [128, 1e-4, (None, None), 128, 264,
                         None, None, None, 1, None]
 
-_train_parameters = ['cache_prefix', 'label_dim', 'epochs', 'sample_interval', 'labels']
+_train_parameters = ['cache_prefix', 'label_dim', 'epochs', 'sample_interval', 'labels', 'critic_iter']
 
 ModelParameters = namedtuple('ModelParameters', _model_parameters, defaults=_model_parameters_df)
-TrainParameters = namedtuple('TrainParameters', _train_parameters, defaults=('', None, 300, 50, None))
+TrainParameters = namedtuple('TrainParameters', _train_parameters, defaults=('', None, 300, 50, None, None))
 
 
 # pylint: disable=R0902
diff --git a/src/ydata_synthetic/synthesizers/timeseries/__init__.py b/src/ydata_synthetic/synthesizers/timeseries/__init__.py
index a3523536..3984a68b 100644
--- a/src/ydata_synthetic/synthesizers/timeseries/__init__.py
+++ b/src/ydata_synthetic/synthesizers/timeseries/__init__.py
@@ -1,5 +1,7 @@
 from ydata_synthetic.synthesizers.timeseries.timegan.model import TimeGAN
+from ydata_synthetic.synthesizers.timeseries.tscwgan.model import TSCWGAN
 
 __all__ = [
     'TimeGAN',
+    'TSCWGAN',
 ]
diff --git a/src/ydata_synthetic/synthesizers/timeseries/tscwgan/__init__.py b/src/ydata_synthetic/synthesizers/timeseries/tscwgan/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/ydata_synthetic/synthesizers/timeseries/tscwgan/model.py b/src/ydata_synthetic/synthesizers/timeseries/tscwgan/model.py
new file mode 100644
index 00000000..ee90c927
--- /dev/null
+++ b/src/ydata_synthetic/synthesizers/timeseries/tscwgan/model.py
@@ -0,0 +1,257 @@
+"""
+Conditional time-series Wasserstein GAN.
+Based on: https://www.naun.org/main/NAUN/neural/2020/a082016-004(2020).pdf
+And on: https://github.com/CasperHogenboom/WGAN_financial_time-series
+"""
+from tqdm import trange
+from numpy import array, vstack, hstack
+from numpy.random import normal
+
+from tensorflow import concat, float32, convert_to_tensor, GradientTape, reduce_mean, tile, squeeze
+from tensorflow import data as tfdata
+from tensorflow.keras import Model, Sequential
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.layers import Input, Conv1D, Dense, LeakyReLU, Flatten, Add
+
+from ydata_synthetic.synthesizers.gan import BaseModel
+from ydata_synthetic.synthesizers import TrainParameters
+from ydata_synthetic.synthesizers.loss import Mode, gradient_penalty
+
+class TSCWGAN(BaseModel):
+
+    __MODEL__='TSCWGAN'
+
+    def __init__(self, model_parameters, gradient_penalty_weight=10):
+        """Create a base TSCWGAN."""
+        self.gradient_penalty_weight = gradient_penalty_weight
+        self.cond_dim = model_parameters.condition
+        super().__init__(model_parameters)
+        self.data_dim = model_parameters.n_cols
+
+    def define_gan(self):
+        self.generator = Generator(self.batch_size). \
+            build_model(input_shape=(self.noise_dim + self.cond_dim, 1), dim=self.layers_dim, data_dim=self.data_dim)
+        self.critic = Critic(self.batch_size). \
+            build_model(input_shape=(self.data_dim + self.cond_dim, 1), dim=self.layers_dim)
+
+        self.g_optimizer = Adam(self.g_lr, beta_1=self.beta_1, beta_2=self.beta_2)
+        self.c_optimizer = Adam(self.d_lr, beta_1=self.beta_1, beta_2=self.beta_2)
+
+        # The generator takes noise as input and generates records
+        noise = Input(shape=self.noise_dim, batch_size=self.batch_size)
+        cond = Input(shape=self.cond_dim, batch_size=self.batch_size)
+        gen = concat([cond, noise], axis=1)
+        gen = self.generator(gen)
+        score = concat([cond, gen], axis=1)
+        score = self.critic(score)
+
+    def train(self, data, train_arguments: TrainParameters):
+        self.define_gan()
+        real_batches = self.get_batch_data(data)
+        noise_batches = self.get_batch_noise()
+
+        for epoch in trange(train_arguments.epochs):
+            for i in range(train_arguments.critic_iter):
+                real_batch = next(real_batches)
+                noise_batch = next(noise_batches)[:len(real_batch)]  # Truncate the noise tensor in the shape of the real data tensor
+
+                c_loss = self.update_critic(real_batch, noise_batch)
+
+            real_batch = next(real_batches)
+            noise_batch = next(noise_batches)[:len(real_batch)]
+
+            g_loss = self.update_generator(real_batch, noise_batch)
+
+            print(f"Epoch: {epoch} | critic_loss: {c_loss} | gen_loss: {g_loss}")
+
+        self.g_optimizer = self.g_optimizer.get_config()
+        self.c_optimizer = self.c_optimizer.get_config()
+
+    def update_critic(self, real_batch, noise_batch):
+        with GradientTape() as c_tape:
+            fake_batch, cond_batch = self._make_fake_batch(real_batch, noise_batch)
+
+            # Real and fake records with conditions
+            real_batch_ = concat([cond_batch, real_batch], axis=1)
+            fake_batch_ = concat([cond_batch, fake_batch], axis=1)
+
+            c_loss = self.c_lossfn(real_batch_, fake_batch_)
+
+        c_gradient = c_tape.gradient(c_loss, self.critic.trainable_variables)
+
+        # Update the weights of the critic using the optimizer
+        self.c_optimizer.apply_gradients(
+            zip(c_gradient, self.critic.trainable_variables)
+        )
+        return c_loss
+
+    def update_generator(self, real_batch, noise_batch):
+        with GradientTape() as g_tape:
+            fake_batch, cond_batch = self._make_fake_batch(real_batch, noise_batch)
+
+            # Fake records with conditions
+            fake_batch_ = concat([cond_batch, fake_batch], axis=1)
+
+            g_loss = self.g_lossfn(fake_batch_)
+
+        g_gradient = g_tape.gradient(g_loss, self.generator.trainable_variables)
+
+        # Update the weights of the generator using the optimizer
+        self.g_optimizer.apply_gradients(
+            zip(g_gradient, self.generator.trainable_variables)
+        )
+        return g_loss
+
+    def c_lossfn(self, real_batch_, fake_batch_):
+        score_fake = self.critic(fake_batch_)
+        score_real = self.critic(real_batch_)
+        grad_penalty = self.gradient_penalty(real_batch_, fake_batch_)
+        c_loss = reduce_mean(score_fake) - reduce_mean(score_real) + grad_penalty
+        return c_loss
+
+    def g_lossfn(self, fake_batch_):
+        score_fake = self.critic(fake_batch_)
+        g_loss = - reduce_mean(score_fake)
+        return g_loss
+
+    def _make_fake_batch(self, real_batch, noise_batch):
+        """Generate a batch of fake records and return it with the batch of used conditions.
+        Conditions are the first elements of records in the real batch."""
+        cond_batch = real_batch[:, :self.cond_dim]
+        gen_input = concat([cond_batch, noise_batch], axis=1)
+        return self.generator(gen_input, training=True), cond_batch
+
+    def gradient_penalty(self, real, fake):
+        gp = gradient_penalty(self.critic, real, fake, mode=Mode.DRAGAN)
+        return gp
+
+    def _generate_noise(self):
+        "Gaussian noise for the generator input."
+        while True:
+            yield normal(size=self.noise_dim)
+
+    def get_batch_noise(self):
+        "Create a batch iterator for the generator gaussian noise input."
+        return iter(tfdata.Dataset.from_generator(self._generate_noise, output_types=float32)
+                                .batch(self.batch_size)
+                                .repeat())
+
+    def get_batch_data(self, data, n_windows= None):
+        if not n_windows:
+            n_windows = len(data)
+        data = squeeze(convert_to_tensor(data, dtype=float32))
+        return iter(tfdata.Dataset.from_tensor_slices(data)
+                                .shuffle(buffer_size=n_windows)
+                                .batch(self.batch_size).repeat())
+
+    def sample(self, condition: array, n_samples: int = 100, seq_len: int = 24):
+        """For a given condition, produce n_samples of length seq_len.
+
+        Args:
+            condition (numpy.array): Condition for the generated samples, must have the same length.
+            n_samples (int): Minimum number of generated samples (returns always a multiple of batch_size).
+            seq_len (int): Length of the generated samples.
+
+        Returns:
+            data (numpy.array): An array of data of shape [n_samples, seq_len]"""
+        assert len(condition.shape) == 2, "Condition array should be two-dimensional."
+        assert condition.shape[1] == self.cond_dim, \
+            f"The condition sequence should have {self.cond_dim} length."
+        batches = n_samples // self.batch_size + 1
+        ar_steps = seq_len // self.data_dim + 1
+        data = []
+        z_dist = self.get_batch_noise()
+        for batch in trange(batches, desc=f'Synthetic data generation'):
+            data_ = []
+            cond_seq = convert_to_tensor(condition, float32)
+            gen_input = concat([tile(cond_seq, multiples=[self.batch_size, 1]), next(z_dist)], axis=1)
+            for step in range(ar_steps):
+                records = self.generator(gen_input, training=False)
+                gen_input = concat([records[:, -self.cond_dim:], next(z_dist)], axis=1)
+                data_.append(records)
+            data_ = hstack(data_)[:, :seq_len]
+            data.append(data_)
+        return array(vstack(data))
+
+
+class Generator(Model):
+    """Conditional generator with skip connections."""
+    def __init__(self, batch_size):
+        self.batch_size = batch_size
+
+    def build_model(self, input_shape, dim, data_dim):
+        # Define input - Expected input shape is (batch_size, seq_len, noise_dim). noise_dim = Z + cond
+        noise_input = Input(shape = input_shape, batch_size = self.batch_size)
+
+        # Compose model
+        proc_input = Sequential(layers=[
+            Conv1D(filters=dim, kernel_size=1, input_shape = input_shape),
+            LeakyReLU(),
+            Conv1D(dim, kernel_size=5, dilation_rate=2, padding="same"),
+            LeakyReLU()
+        ], name='input_to_latent')(noise_input)
+
+        block_cnn = Sequential(layers=[
+            Conv1D(filters=dim, kernel_size=3, dilation_rate=2, padding="same"),
+            LeakyReLU()
+        ], name='block_cnn')
+        for i in range(3):
+            if i == 0:
+                cnn_block_i = proc_input
+                cnn_block_o = block_cnn(proc_input)
+            else:
+                cnn_block_o = block_cnn(cnn_block_i)
+            cnn_block_i = Add()([cnn_block_i, cnn_block_o])
+
+        shift = Sequential(layers=[
+            Conv1D(filters=10, kernel_size=3, dilation_rate=2, padding="same"),
+            LeakyReLU(),
+            Flatten(),
+            Dense(dim*2),
+            LeakyReLU()
+        ], name='block_shift')(cnn_block_i)
+
+        block = Sequential(layers=[
+            Dense(dim*2),
+            LeakyReLU()
+        ], name='block')
+        for i in range(3):
+            if i == 0:
+                block_i = shift
+                block_o = block(shift)
+            else:
+                block_o = block(block_i)
+            block_i = Add()([block_i, block_o])
+
+        output = Dense(data_dim, name='latent_to_ouput')(block_i)
+        return Model(inputs = noise_input, outputs = output, name='SkipConnectionGenerator')
+
+class Critic(Model):
+    """Conditional Wasserstein Critic with skip connections."""
+    def __init__(self, batch_size):
+        self.batch_size = batch_size
+
+    def build_model(self, input_shape, dim):
+        # Define input - Expected input shape is X + condition
+        record_input = Input(shape = input_shape, batch_size = self.batch_size)
+
+        # Compose model
+        proc_record = Sequential(layers=[
+            Dense(dim*2,),
+            LeakyReLU()
+        ], name='ts_to_latent')(record_input)
+
+        block = Sequential(layers=[
+            Dense(dim*2),
+            LeakyReLU()
+        ], name='block')
+        for i in range(7):
+            if i == 0:
+                block_i = proc_record
+                block_o = block(proc_record)
+            else:
+                block_o = block(block_i)
+            block_i = Add()([block_i, block_o])
+
+        output = Dense(1, name = 'latent_to_score')(block_i)
+        return Model(inputs=record_input, outputs=output, name='SkipConnectionCritic')