diff --git a/.gitignore b/.gitignore
index 770002a..0126e61 100644
--- a/.gitignore
+++ b/.gitignore
@@ -172,3 +172,5 @@ dmypy.json
 # Pyre type checker
 .pyre/
 
+# Temporary files
+temp.py
\ No newline at end of file
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..7c2feb7
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,3 @@
+{
+  "editor.formatOnSave": false
+}
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index e0a93e5..eac021d 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -109,4 +109,18 @@ To apply the new logic in the library, it will be necessary to implement the use
 
 ### Additional Features
 
-Preferably, use the `_bibmon_tools.py` file to implement additional features.
\ No newline at end of file
+Preferably, use the `_bibmon_tools.py` file to implement additional features.
+
+### Testing New Functionalities
+
+The first step to add new functionalities is to download the testing libraries. To do this, run the following command:
+
+```bash
+pip install -r test/requirements.txt
+```
+
+After implementing the new functionalities, run the tests to ensure that the new code is working correctly. To do this, run the following command:
+
+```bash
+pytest
+```
\ No newline at end of file
diff --git a/bibmon/__init__.py b/bibmon/__init__.py
index 3dd3ee7..1d783ab 100644
--- a/bibmon/__init__.py
+++ b/bibmon/__init__.py
@@ -4,12 +4,12 @@
 from ._sbm import SBM
 from ._sklearn_regressor import sklearnRegressor
 from ._preprocess import PreProcess
-from ._load_data import load_tennessee_eastman, load_real_data
+from ._load_data import load_tennessee_eastman, load_real_data, load_3w
 from ._bibmon_tools import train_val_test_split, complete_analysis, comparative_table, spearmanr_dendrogram, create_df_with_dates, create_df_with_noise, align_dfs_by_rows
 
 __all__ = ['Autoencoder','PCA','ESN','SBM',
 	   'sklearnRegressor', 'PreProcess',
-           'load_tennessee_eastman', 'load_real_data', 
+           'load_tennessee_eastman', 'load_real_data', 'load_3w', 
            'train_val_test_split', 'complete_analysis', 'comparative_table',
 	       'spearmanr_dendrogram', 'create_df_with_dates',
            'create_df_with_noise', 'align_dfs_by_rows']
diff --git a/bibmon/_bibmon_tools.py b/bibmon/_bibmon_tools.py
index fd598d9..210b961 100644
--- a/bibmon/_bibmon_tools.py
+++ b/bibmon/_bibmon_tools.py
@@ -2,6 +2,7 @@
 import pandas as pd
 from datetime import datetime
 import matplotlib.pyplot as plt
+from typing import Literal
 
 ###############################################################################
 
@@ -692,4 +693,86 @@ def comparative_table (models, X_train, X_validation, X_test,
         
         return_tables.append(times_df)
         
-    return return_tables
\ No newline at end of file
+    return return_tables
+
+##############################################################################
+
+def find_df_transitions(
+    df: pd.DataFrame,
+    threshold: float = 1,
+    data_type: Literal["string", "number"] = "number",
+    label: str = None,
+) -> list[int]:
+    """
+    Finds transitions in a DataFrame. This can be used to find indices of interesting events in the data.
+
+    Parameters
+    ----------
+    df: pandas.DataFrame
+        Data to be analyzed.
+    threshold: float, optional
+        Threshold to be used in the transition detection, this is the minimum difference between two consecutive points. Will be used only if data_type is 'number'.
+    data_type: str, optional
+        Type of data to be analyzed. If 'number', the threshold will be used to detect transitions. If 'string', the function will look for changes in the values.
+    label: str
+        Label to be used in the transition detection.
+
+    Returns
+    ----------
+    : list of ints
+        Indices of the transitions.
+    """
+
+    if label is None:
+        return []
+
+    transitions = []
+    previous_event = df[label].iloc[0]
+
+    for i in range(1, len(df)):
+        if data_type == "number":
+            if abs(df[label].iloc[i] - previous_event) > threshold:
+                transitions.append(i)
+                previous_event = df[label].iloc[i]
+        elif data_type == "string":
+            if df[label].iloc[i] != previous_event:
+                transitions.append(i)
+                previous_event = df[label].iloc[i]
+
+    return transitions
+
+###############################################################################
+
+def split_df_percentages(df: pd.DataFrame, percentages: list[float]) -> list[pd.DataFrame]:
+    """
+    Splits a DataFrame into multiple DataFrames according to the given percentages, the sum of percentages must equal 1.
+
+    For example, if percentage = [0.6, 0.2, 0.2], the function will return a list with three DataFrames, the first one with 60% of the data, the second one with 20% and the third one with 20%.
+
+    Warning: This function may cause data loss if the split cannot be done exactly according to the percentages.
+
+    Parameters
+    ----------
+    df: pandas.DataFrame
+        Data to be split.
+    percentages: list of floats
+        List of percentages to be used in the split.
+
+    Returns
+    ----------
+    : list of pandas.DataFrames
+        List with the split DataFrames.
+    """
+
+    if sum(percentages) != 1:
+        raise ValueError("The sum of the percentages must be 1.")
+
+    split_dfs = []
+    start = 0
+
+    for i in range(len(percentages)):
+        end = start + int(percentages[i] * len(df))
+        split_dfs.append(df.iloc[start:end])
+        start = end
+
+    return split_dfs
\ No newline at end of file
diff --git a/bibmon/_gridsearch.py b/bibmon/_gridsearch.py
new file mode 100644
index 0000000..08f8a62
--- /dev/null
+++ b/bibmon/_gridsearch.py
@@ -0,0 +1,259 @@
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.ensemble import RandomForestRegressor
+import pandas as pd
+import joblib
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import cross_val_score
+import xgboost as xgb
+
+class GridSearchModel:
+    """
+    Class used to encapsulate the grid search process for hyperparameter tuning.
+    
+    Parameters
+    ----------
+    model : estimator object
+        The base model to perform grid search on (e.g., RandomForestRegressor).
+        
+    param_grid : dict, optional
+        The grid of parameters to search over. If not provided, a default
+        parameter grid for RandomForestRegressor will be used.
+        
+    scoring : string, optional
+        The scoring method to use for evaluating the models.
+        
+    cv : int, optional
+        Number of cross-validation folds.
+    
+    test_size : float, optional
+        The percentage of data used for testing, default is 0.1 (10%).
+    
+    Attributes
+    ----------
+    best_params_ : dict
+        The best parameters found by the grid search.
+    
+    best_estimator_ : estimator object
+        The estimator that was chosen by the grid search.
+    
+    Methods
+    ----------
+    fit(df, target_column):
+        Fits the grid search using the provided data.
+    """
+
+    def __init__(self, model=None, param_grid=None, scoring='neg_mean_absolute_error', cv=5, test_size=0.01):
+        self.model = model if model is not None else RandomForestRegressor(n_jobs=-1, random_state=42)
+        
+        # Param_grid default
+        default_param_grid = {
+            'n_estimators': [10, 100],
+            'max_depth': [None, 10],
+            'min_samples_split': [2, 4],
+        }
+        self.param_grid = param_grid if param_grid is not None else default_param_grid
+        
+        self.scoring = scoring
+        self.cv = cv
+        self.test_size = test_size  # Using only 10% of the data for testing
+        self.grid_search = None
+        self.best_params_ = None
+        self.best_estimator_ = None
+
+    def fit(self, df: pd.DataFrame, target_column: str):
+        """
+        Fit the grid search model on the provided data.
+        
+        Parameters
+        ----------
+        df : pandas.DataFrame
+            The dataset including features and target column.
+            
+        target_column : str
+            The name of the column to be predicted (target variable).
+        """
+        X = df.drop(columns=[target_column])
+        Y = df[target_column]
+        
+        
+        X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=self.test_size, random_state=42)
+        
+        self.grid_search = GridSearchCV(
+            estimator=self.model,
+            param_grid=self.param_grid,
+            scoring=self.scoring,
+            cv=self.cv,
+            n_jobs=-1
+        )
+        
+        self.grid_search.fit(X_train, Y_train)
+        
+        self.best_params_ = self.grid_search.best_params_
+        self.best_estimator_ = self.grid_search.best_estimator_
+        
+        return self.best_estimator_
+    
+    def predict(self, X):
+        """
+        Make predictions using the best estimator found by grid search.
+
+        Parameters
+        ----------
+        X : pandas.DataFrame or numpy.ndarray
+            Input data for making predictions.
+
+        Returns
+        ----------
+        y_pred : numpy.ndarray
+            Predicted values.
+        """
+        if self.best_estimator_ is None:
+            raise ValueError("The model has not been fitted yet. Call the `fit` method first.")
+        return self.best_estimator_.predict(X)
+
+    def score(self, X_test, y_test):
+        """
+        Evaluate the best estimator's performance on the test set.
+
+        Parameters
+        ----------
+        X_test : pandas.DataFrame or numpy.ndarray
+            Test features.
+        y_test : pandas.Series or numpy.ndarray
+            True values for the test set.
+
+        Returns
+        ----------
+        score : float
+            The score of the best model on the test set based on the defined scoring metric.
+        """
+        if self.best_estimator_ is None:
+            raise ValueError("The model has not been fitted yet. Call the `fit` method first.")
+        return self.best_estimator_.score(X_test, y_test)
+    
+    def get_best_params(self):
+        """
+        Get the best hyperparameters found by grid search.
+
+        Returns
+        ----------
+        best_params : dict
+            Dictionary of the best hyperparameters.
+        """
+        if self.best_params_ is None:
+            raise ValueError("Grid search has not been performed yet. Call the `fit` method first.")
+        return self.best_params_
+
+
+    def save_model(self, filename):
+        """
+        Save the best model to a file.
+
+        Parameters
+        ----------
+        filename : str
+            Path where the model should be saved.
+
+        Returns
+        ----------
+        None
+        """
+        if self.best_estimator_ is None:
+            raise ValueError("The model has not been fitted yet. Call the `fit` method first.")
+        joblib.dump(self.best_estimator_, filename)
+        
+    def load_model(self, filename):
+        """
+        Load a model from a file.
+        
+        Parameters
+        ----------
+        filename : str
+            Path to the saved model file.
+            
+        Returns
+        ----------
+        None
+        """
+        if self.best_estimator_ is None:
+            raise ValueError("The model has not been fitted yet. Call the `fit` method first.")
+        self.best_estimator_ = joblib.load(filename)
+        
+    
+    def plot_feature_importance(self, feature_names):
+        """
+        Plot the feature importance from the best estimator.
+
+        Parameters
+        ----------
+        feature_names : list
+            List of feature names in the same order as they appear in the dataset.
+
+        Returns
+        ----------
+        None
+        """
+        if self.best_estimator_ is None:
+            raise ValueError("The model has not been fitted yet. Call the `fit` method first.")
+        
+        importances = self.best_estimator_.feature_importances_
+        indices = np.argsort(importances)[::-1]
+
+        plt.figure(figsize=(10, 6))
+        plt.title("Feature Importance")
+        plt.bar(range(len(importances)), importances[indices], align='center')
+        plt.xticks(range(len(importances)), [feature_names[i] for i in indices], rotation=90)
+        plt.tight_layout()
+        plt.show()
+        
+    def cross_val_score_summary(self, X, Y):
+        """
+        Get a summary of cross-validation scores for the best model.
+
+        Parameters
+        ----------
+        X : pandas.DataFrame
+            Input data (features).
+        Y : pandas.Series
+            Target variable.
+
+        Returns
+        ----------
+        summary : dict
+            Dictionary with mean and standard deviation of cross-validation scores.
+        """
+        if self.best_estimator_ is None:
+            raise ValueError("The model has not been fitted yet. Call the `fit` method first.")
+        
+        scores = cross_val_score(self.best_estimator_, X, Y, cv=self.cv, scoring=self.scoring)
+        return {
+            'mean_score': scores.mean(),
+            'std_dev': scores.std(),
+            'scores': scores
+        }
+
+    def xgboost_best(self, df: pd.DataFrame, target_column: str):
+        """Perform grid search to find the best parameters for XGBRegressor."""
+        X = df.drop(columns=[target_column])
+        Y = df[target_column]
+        X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=self.test_size, random_state=42)
+
+        param_grid = {
+            'n_estimators': [100, 200],
+            'learning_rate': [ 0.05, 0.1],
+            'max_depth': [None, 6],
+            'subsample': [0.1,0.5,],
+            'colsample_bytree': [0.1,0.5],
+            'gamma': [0, 0.1],
+            'reg_alpha': [0, 0.1],
+            'reg_lambda': [0.1, 0.5]
+        }
+
+        model = xgb.XGBRegressor(tree_method='gpu_hist', predictor='gpu_predictor', random_state=42)
+        self.grid_search = GridSearchCV(estimator=model, param_grid=param_grid, scoring=self.scoring, cv=self.cv, n_jobs=-1)
+        self.grid_search.fit(X_train, Y_train)
+        self.best_params_ = self.grid_search.best_params_
+        self.best_estimator_ = self.grid_search.best_estimator_
+
+        return self.best_estimator_
\ No newline at end of file
diff --git a/bibmon/_load_data.py b/bibmon/_load_data.py
index 85d160f..bcb2f3b 100644
--- a/bibmon/_load_data.py
+++ b/bibmon/_load_data.py
@@ -1,13 +1,15 @@
 import os
 import pandas as pd
 import importlib.resources as pkg_resources
+from typing import Literal
 
-from ._bibmon_tools import create_df_with_dates
-from . import real_process_data, tennessee_eastman 
+from . import _bibmon_tools as b_tools
+from . import real_process_data, tennessee_eastman, three_w
 
 ###############################################################################
 
-def load_tennessee_eastman (train_id = 0, test_id = 0):
+
+def load_tennessee_eastman(train_id=0, test_id=0):
     """
     Load the 'Tennessee Eastman Process' benchmark data.
 
@@ -20,71 +22,110 @@ def load_tennessee_eastman (train_id = 0, test_id = 0):
         Identifier of the test data.
         No fault: 0. With faults: 1 to 20.
     Returns
-    ----------                
+    ----------
     train_df: pandas.DataFrame
         Training data.
     test_df: pandas.DataFrame
         Test data.
-    """    
-        
-    tags1 = ['XMEAS('+str(ii)+')' for ii in range(1,42)]
-    tags2 = ['XMV('+str(ii)+')' for ii in range(1,12)]
+    """
+
+    tags1 = ["XMEAS(" + str(ii) + ")" for ii in range(1, 42)]
+    tags2 = ["XMV(" + str(ii) + ")" for ii in range(1, 12)]
     tags = tags1 + tags2
-    
-    file_train = f'd{train_id}.dat'
-    file_test = f'd{test_id}_te.dat'
+
+    file_train = f"d{train_id}.dat"
+    file_test = f"d{test_id}_te.dat"
 
     if len(file_train) == 6:
-        file_train = file_train[:2]+'0'+file_train[2:]
-        
+        file_train = file_train[:2] + "0" + file_train[2:]
+
     if len(file_test) == 9:
-        file_test = file_test[:1]+'0'+file_test[1:]
+        file_test = file_test[:1] + "0" + file_test[1:]
 
     with pkg_resources.path(tennessee_eastman, file_train) as filepath:
 
-        if file_train == 'd00.dat':
-            
-            tmp1 = pd.read_csv(filepath,sep='\t',
-                               names=['0'])
-            tmp2 = pd.DataFrame([tmp1.T.iloc[0,i].strip() for 
-                                i in range(tmp1.shape[0])])
+        if file_train == "d00.dat":
+
+            tmp1 = pd.read_csv(filepath, sep="\t", names=["0"])
+            tmp2 = pd.DataFrame(
+                [tmp1.T.iloc[0, i].strip() for i in range(tmp1.shape[0])]
+            )
             train_df = pd.DataFrame()
 
             for ii in range(52):
-                train_df[tags[ii]]=[float(s) for s in tmp2[0][ii].split('  ')]
-                
-            train_df = create_df_with_dates(train_df, 
-                                            freq = '3min')
-            
+                train_df[tags[ii]] = [float(s) for s in tmp2[0][ii].split("  ")]
+
+            train_df = b_tools.create_df_with_dates(train_df, freq="3min")
+
         else:
 
-            train_df = create_df_with_dates(pd.read_csv(filepath, 
-                                                        sep = '\s+',
-                                                        names = tags),
-                                            freq = '3min')
+            train_df = b_tools.create_df_with_dates(
+                pd.read_csv(filepath, sep="\s+", names=tags), freq="3min"
+            )
 
     with pkg_resources.path(tennessee_eastman, file_test) as filepath:
 
-        test_df = create_df_with_dates(pd.read_csv(filepath, 
-                                                   sep = '\s+', 
-                                                   names = tags),
-                                       start = '2020-02-01 00:00:00',
-                                       freq = '3min')
+        test_df = b_tools.create_df_with_dates(
+            pd.read_csv(filepath, sep="\s+", names=tags),
+            start="2020-02-01 00:00:00",
+            freq="3min",
+        )
 
     return train_df, test_df
 
+
 ###############################################################################
 
-def load_real_data ():
+
+def load_real_data():
     """
     Load a sample of real process data.
     The variables have been anonymized for availability in the library.
-    
+
     Returns
-    ----------                
+    ----------
     : pandas.DataFrame
         Process data.
-    """    
+    """
+
+    with pkg_resources.path(real_process_data, "real_process_data.csv") as file:
+        return pd.read_csv(file, index_col=0, parse_dates=True)
+
+
+###############################################################################
+
+AVAILABLE_3W_CLASSES = ["8"]
+
+
+def load_3w(dataset_class: Literal["8"] = "8"):
+    """
+    Load the '3W-8' benchmark data.
+
+    Parameters
+    ----------
+    dataset_class: string
+        Identifier of the dataset class.
+        Available classes: '8'.
+    Returns
+    ----------
+    : pandas.DataFrame
+        Process data.
+    : configparser.ConfigParser
+        Configuration file.
+    """
+
+    if dataset_class != "8":
+        raise ValueError(
+            f"Dataset class not available. Available classes: {AVAILABLE_3W_CLASSES}"
+        )
 
-    with pkg_resources.path(real_process_data,'real_process_data.csv') as file:
-        return pd.read_csv(file,index_col = 0, parse_dates = True)
\ No newline at end of file
+    with pkg_resources.path(three_w, "WELL-00019_20120601165020.parquet") as file:
+        with pkg_resources.path(three_w, "dataset.ini") as path:
+            ini = three_w.tools.load_dataset_ini(path)
+            return (
+                pd.read_parquet(
+                    file,
+                    engine=ini.get("PARQUET_SETTINGS", "PARQUET_ENGINE"),
+                ),
+                ini,
+            )
diff --git a/bibmon/_preprocess.py b/bibmon/_preprocess.py
index 0a85adb..45890e8 100644
--- a/bibmon/_preprocess.py
+++ b/bibmon/_preprocess.py
@@ -1,7 +1,7 @@
 import copy
 import pandas as pd
 import statsmodels.tsa.tsatools
-
+import numpy as np
 ###############################################################################
 
 class PreProcess ():
@@ -391,4 +391,112 @@ def moving_average_filter (self, df,  train_or_test = 'train', WS = 10):
         if hasattr(df,'name'):
             new_df.name = df.name
                         
-        return new_df.drop(df.index[:WS])
\ No newline at end of file
+        return new_df.drop(df.index[:WS])
+
+    def time_based_windowing(self, df, train_or_test='train', window_size='30min', agg_func= "max"):
+        """
+        Applies a rolling window based on time, where each window is determined by the timestamp index.
+        
+        Parameters
+        ----------
+        df: pandas.DataFrame
+            Data to be processed.
+        train_or_test: string, optional
+            Indicates which step the data corresponds to.
+        window_size: string, optional
+            Size of the time window (e.g., '30min' for 30 minutes, '1H' for 1 hour).
+        agg_func: string, optional
+            Aggregation function to apply within each window ('mean', 'sum', 'max', 'min', etc.).
+            
+        Returns
+        ----------                
+        : pandas.DataFrame
+            Processed data.
+        """
+        
+        if not pd.api.types.is_datetime64_any_dtype(df.index):
+            raise ValueError("DataFrame index must be a datetime type.")
+
+        if agg_func == 'mean':
+            return df.resample(window_size).mean()
+        elif agg_func == 'sum':
+            return df.resample(window_size).sum()
+        elif agg_func == 'max':
+            return df.resample(window_size).max()
+        elif agg_func == 'min':
+            return df.resample(window_size).min()
+        else:
+            raise ValueError("Unsupported aggregation function. Choose from 'mean', 'sum', 'max', or 'min'.")
+        
+        
+    def remove_constant_difference(self, df,train_or_test='train'):
+        """
+        Calculate the cumulative sum.
+        Drop columns with constant difference.
+
+        Parameters
+        ----------
+        df: pandas.DataFrame
+            Data to be processed.
+        train_or_test: string, optional
+            Indicates which step the data corresponds to.
+
+        Returns
+        -------
+        pandas.DataFrame
+            Processed DataFrame without columns containing constant difference.
+        """
+        cumulative_df = df.cumsum()
+        columns_to_drop = []
+
+        for column in cumulative_df.columns:
+            if not pd.api.types.is_numeric_dtype(cumulative_df[column]):
+                raise TypeError(f"'{column}' is not numeric type.")
+
+            diff = cumulative_df[column].diff()
+
+            # Verify constant difference
+            if len(diff.dropna()) > 1 and diff.nunique() == 1:
+                columns_to_drop.append(column)  
+
+        cumulative_df.drop(columns=columns_to_drop, inplace=True)
+        return cumulative_df
+
+    def differencing(self, df, train_or_test='train', lag=1):
+        """
+        Apply differencing to remove trend or seasonality.
+        
+        Parameters
+        ----------
+        df: pandas.DataFrame
+            Data to be processed.
+        train_or_test: string, optional
+            Indicates which step the data corresponds to.
+        lag: int, optional
+            Lag to be used for differencing.
+        Returns
+        ----------
+        : pandas.DataFrame
+        Processed data.
+        """
+        return df.diff(periods=lag).dropna()
+    
+
+
+    def log_transform(self, df, train_or_test='train'):
+        """
+        Apply log transformation to reduce variability.
+
+        Parameters
+        ----------
+        df: pandas.DataFrame
+            Data to be processed.
+        train_or_test: string, optional
+            Indicates which step the data corresponds to.
+
+        Returns
+        ----------
+        : pandas.DataFrame
+        Processed data.
+        """
+        return df.apply(lambda x: np.log1p(x))
\ No newline at end of file
diff --git a/bibmon/three_w/LICENSE-CC-BY b/bibmon/three_w/LICENSE-CC-BY
new file mode 100644
index 0000000..52bd145
--- /dev/null
+++ b/bibmon/three_w/LICENSE-CC-BY
@@ -0,0 +1,395 @@
+Attribution 4.0 International
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\ No newline at end of file
diff --git a/bibmon/three_w/README.md b/bibmon/three_w/README.md
new file mode 100644
index 0000000..d37506f
--- /dev/null
+++ b/bibmon/three_w/README.md
@@ -0,0 +1,83 @@
+[![CC BY 4.0][cc-by-shield]][cc-by]
+[![Versioning][semver-shield]][semver]
+
+[cc-by]: http://creativecommons.org/licenses/by/4.0/
+[cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg
+[semver]: https://semver.org
+[semver-shield]: https://img.shields.io/badge/semver-2.0.0-blue
+
+# Table of Content
+
+* [Introduction](#introduction)
+* [Release Notes](#release-notes)
+  * [1.0.0](#100)
+  * [1.1.0](#110)
+  * [1.1.1](#111)
+  * [2.0.0](#200)
+
+# Introduction
+
+All 3W Dataset data files (files in the subdirectories of the [dataset](dataset) directory) are licensed under the [Creative Commons Attribution 4.0 International License][cc-by].
+
+# Release Notes
+
+Each subsection below contains release notes for a specific 3W Dataset version. Differences from the immediately previous version are highlighted.
+
+## 1.0.0
+
+Release: July 1, 2019.
+
+This was the first published version, which is fully described in [this](https://doi.org/10.1016/j.petrol.2019.106223) paper.
+
+## 1.1.0
+
+Release: December 30, 2022.
+
+Highlights:
+
+1. New instances were added as follows:
+    * 1 instance of event type 7.
+1. Instances were removed due to issues identified as follows:
+	* 3 instances of event type 0;
+	* 1 instance of event type 5;
+	* 3 instances of event type 8, when compared to what is described in the paper **A realistic and public dataset with rare undesirable real events in oil wells** published in the **Journal of Petroleum Science and Engineering** (link [here](https://doi.org/10.1016/j.petrol.2019.106223)).
+1. Normal periods of certain instances with anomalies were increased as possible. We tried to have instances with minimum normal periods of 1 hour;
+1. Names of certain files with instances have changed due to increased normal periods;
+1. Labels in some real instances were adjusted by experts;
+1. All values of some variables in some real instances were corrected due to corrections in historian systems' tag configurations;
+1. Certain variable values ​​have undergone minimal change due to different rounding;
+1. The 3W Dataset's main configuration file ([dataset.ini](dataset.ini)) was updated.
+
+## 1.1.1
+
+Release: April 09, 2023.
+
+Highlights:
+
+1. Issue #60 was resolved;
+1. Issue #65 was resolved;
+1. Certain variable values ​​have undergone minimal change due to different rounding;
+1. The 3W Dataset's main configuration file ([dataset.ini](dataset.ini)) was updated.
+
+## 2.0.0
+
+Release: July 25, 2024.
+
+Highlights:
+
+1. All instances are now saved in Parquet files (created with the `pyarrow` engine and `brotli` compression);
+1. Reduction in disk space occupied by the 3W Dataset of 3.15 GB (from 4.89 GB to 1.74 GB);
+1. Real and simulated instances of type 9 were added;
+1. Several instances of types 0, 3, 4, 5, 6 and 8 were added;
+1. Another 24 real wells were covered with new real instances (now 42 real wells are covered);
+1. Some real instances, mainly of type 1, were removed;
+1. 1 variable was removed (`T-JUS-CKGL`);
+1. Another 20 variables were added (there are now 27 variables);
+1. Another label referring to well operational status was added;
+1. Normal periods in several real instances with unwanted events were extended;
+1. All labeling gaps in real instances were eliminated (all observations were labeled);
+1. Conversions between measurement units in several instances were corrected;
+1. Labels in several real instances were adjusted by experts;
+1. All values of some variables in some real instances were corrected due to corrections in historian systems' tag configurations;
+1. Certain variable values ​​have undergone minimal change due to different rounding;
+1. The 3W Dataset's main configuration file ([dataset.ini](dataset.ini)) was updated.
\ No newline at end of file
diff --git a/bibmon/three_w/WELL-00019_20120601165020.parquet b/bibmon/three_w/WELL-00019_20120601165020.parquet
new file mode 100644
index 0000000..abce3e4
Binary files /dev/null and b/bibmon/three_w/WELL-00019_20120601165020.parquet differ
diff --git a/bibmon/three_w/__init__.py b/bibmon/three_w/__init__.py
new file mode 100644
index 0000000..fed9bd2
--- /dev/null
+++ b/bibmon/three_w/__init__.py
@@ -0,0 +1 @@
+from . import tools
\ No newline at end of file
diff --git a/bibmon/three_w/dataset.ini b/bibmon/three_w/dataset.ini
new file mode 100644
index 0000000..d8e271f
--- /dev/null
+++ b/bibmon/three_w/dataset.ini
@@ -0,0 +1,140 @@
+# 3W Dataset's main configuration file.
+#
+# All settings inherent in the 3W Dataset that can be used by your 
+# consumers, including the 3W Toolkit, are maintained in this file.
+# In this file, we use the configuration language supported by the 
+# configparser module.
+
+# Versions in gereral
+#
+[VERSION]
+# 3W Dataset version (may be different than 3W Toolkit version)
+DATASET = 2.0.0
+
+# Parquet files properties in general
+#
+[PARQUET_FILE_PROPERTIES]
+timestamp = Instant at which observation was generated
+ABER-CKGL = Opening of the GLCK (gas lift choke) [%%]
+ABER-CKP = Opening of the PCK (production choke) [%%]
+ESTADO-DHSV = State of the DHSV (downhole safety valve) [0, 0.5, or 1]
+ESTADO-M1 = State of the PMV (production master valve) [0, 0.5, or 1]
+ESTADO-M2 = State of the AMV (annulus master valve) [0, 0.5, or 1]
+ESTADO-PXO = State of the PXO (pig-crossover) valve [0, 0.5, or 1]
+ESTADO-SDV-GL = State of the gas lift SDV (shutdown valve) [0, 0.5, or 1]
+ESTADO-SDV-P = State of the production SDV (shutdown valve) [0, 0.5, or 1]
+ESTADO-W1 = State of the PWV (production wing valve) [0, 0.5, or 1]
+ESTADO-W2 = State of the AWV (annulus wing valve) [0, 0.5, or 1]
+ESTADO-XO = State of the XO (crossover) valve [0, 0.5, or 1]
+P-ANULAR = Pressure in the well annulus [Pa]
+P-JUS-BS = Downstream pressure of the SP (service pump) [Pa]
+P-JUS-CKGL = Downstream pressure of the GLCK (gas lift choke) [Pa]
+P-JUS-CKP = Downstream pressure of the PCK (production choke) [Pa]
+P-MON-CKGL = Upstream pressure of the GLCK (gas lift choke) [Pa]
+P-MON-CKP = Upstream pressure of the PCK (production choke) [Pa]
+P-MON-SDV-P = Upstream pressure of the production SDV (shutdown valve) [Pa]
+P-PDG = Pressure at the PDG (permanent downhole gauge) [Pa]
+PT-P = Downstream pressure of the PWV (production wing valve) in the production tube [Pa]
+P-TPT = Pressure at the TPT (temperature and pressure transducer) [Pa]
+QBS = Flow rate at the SP (service pump) [m3/s]
+QGL = Gas lift flow rate [m3/s]
+T-JUS-CKP = Downstream temperature of the PCK (production choke) [oC]
+T-MON-CKP = Upstream temperature of the PCK (production choke) [oC]
+T-PDG = Temperature at the PDG (permanent downhole gauge) [oC]
+T-TPT = Temperature at the TPT (temperature and pressure transducer) [oC]
+class = Label of the observation
+state = Well operational status
+
+# Common properties of all event types covered by the 3W Project
+#
+[EVENTS]
+# Internal names of all event types
+NAMES = NORMAL, ABRUPT_INCREASE_OF_BSW, SPURIOUS_CLOSURE_OF_DHSV, 
+    SEVERE_SLUGGING, FLOW_INSTABILITY, RAPID_PRODUCTIVITY_LOSS, 
+    QUICK_RESTRICTION_IN_PCK, SCALING_IN_PCK, 
+    HYDRATE_IN_PRODUCTION_LINE, HYDRATE_IN_SERVICE_LINE
+# Simulated and hand-drawn instances
+EXTRA_INSTANCES_TRAINING = -1 
+# Offset between an undesirable event's label and its transient label
+TRANSIENT_OFFSET = 100
+
+# This section defines default properties for a specific event type
+#
+[NORMAL]
+LABEL = 0
+DESCRIPTION = Normal Operation
+
+# This section defines default properties for a specific event type
+#
+[ABRUPT_INCREASE_OF_BSW]
+LABEL = 1
+DESCRIPTION = Abrupt Increase of BSW
+TRANSIENT = True
+
+# This section defines default properties for a specific event type
+#
+[SPURIOUS_CLOSURE_OF_DHSV]
+LABEL = 2
+DESCRIPTION = Spurious Closure of DHSV
+TRANSIENT = True
+WINDOW = 180
+STEP = 15
+ 
+# This section defines default properties for a specific event type
+#
+[SEVERE_SLUGGING]
+LABEL = 3
+DESCRIPTION = Severe Slugging
+TRANSIENT = False
+
+# This section defines default properties for a specific event type
+#
+[FLOW_INSTABILITY]
+LABEL = 4
+DESCRIPTION = Flow Instability
+TRANSIENT = False
+
+# This section defines default properties for a specific event type
+#
+[RAPID_PRODUCTIVITY_LOSS]
+LABEL = 5
+DESCRIPTION = Rapid Productivity Loss
+TRANSIENT = True
+
+# This section defines default properties for a specific event type
+#
+[QUICK_RESTRICTION_IN_PCK]
+LABEL = 6
+DESCRIPTION = Quick Restriction in PCK
+TRANSIENT = True
+
+# This section defines default properties for a specific event type
+#
+[SCALING_IN_PCK]
+LABEL = 7
+DESCRIPTION = Scaling in PCK
+TRANSIENT = True
+
+# This section defines default properties for a specific event type
+#
+[HYDRATE_IN_PRODUCTION_LINE]
+LABEL = 8
+DESCRIPTION = Hydrate in Production Line
+TRANSIENT = True
+
+# This section defines default properties for a specific event type
+#
+[HYDRATE_IN_SERVICE_LINE]
+LABEL = 9
+DESCRIPTION = Hydrate in Service Line
+TRANSIENT = True
+
+# Settings related to the Parquet format
+# 
+[PARQUET_SETTINGS]
+# Used/chosen extension
+PARQUET_EXTENSION = .parquet
+# Used/chosen extension engine
+PARQUET_ENGINE = pyarrow
+# Used/chosen extension compression
+PARQUET_COMPRESSION = brotli
\ No newline at end of file
diff --git a/bibmon/three_w/tools.py b/bibmon/three_w/tools.py
new file mode 100644
index 0000000..a73ced2
--- /dev/null
+++ b/bibmon/three_w/tools.py
@@ -0,0 +1,99 @@
+import configparser
+import pandas as pd
+from .. import _preprocess as preproc
+from .. import _bibmon_tools as b_tools
+from typing import Literal, Tuple
+
+
+def load_dataset_ini(dataset_ini_path) -> configparser.ConfigParser:
+    """
+    Loads the dataset.ini file.
+
+    Parameters
+    ----------
+    dataset_ini_path: string
+        Path to the dataset.ini file.
+    Returns
+    ----------
+    : configparser.ConfigParser
+        Configuration file.
+    """
+
+    config = configparser.ConfigParser()
+    config.read(dataset_ini_path)
+
+    return config
+
+
+###############################################################################
+
+
+def split_dataset(
+    DataFrame: pd.DataFrame,
+    config_file: configparser.ConfigParser,
+    training_percentage: float = 0.8,
+    validation_percentage: float = 0.2,
+    split_on: Literal["transition_state", "error_state"] = "transition_state",
+) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+    """
+    Splits a DataFrame into training, validation and test sets.
+
+    The split is done according to the transition_state or error_state, the sum of training and validation percentages must equal 1.
+
+    Parameters
+    ----------
+    DataFrame: pandas.DataFrame
+        Data to be split.
+    config_file: configparser.ConfigParser
+        Configuration file.
+    training_percentage: float
+        Percentage of the training set.
+    validation_percentage: float
+        Percentage of the validation set.
+    split_on: string
+        Split the data on the transition_state or error_state.
+    Returns
+    ----------
+    : tuple of pandas.DataFrames
+        Training, validation and test sets.
+    """
+
+    if training_percentage + validation_percentage != 1:
+        raise ValueError(
+            "The sum of training and validation percentages must equals 1."
+        )
+
+    df_processed = preproc.PreProcess(f_pp=["ffill_nan"]).apply(DataFrame)
+
+    transitions = b_tools.find_df_transitions(df_processed, 1, "number", "class")
+
+    split_index = 0
+
+    for t in transitions:
+        if t == 0:
+            continue
+
+        transition_class = int(df_processed.iloc[t]["class"])
+        transient_offset = int(config_file.get("EVENTS", "TRANSIENT_OFFSET"))
+
+        if transition_class > transient_offset and split_on == "transition_state":
+            split_index = t
+            break
+        if transition_class < transient_offset and split_on == "error_state":
+            split_index = t
+            break
+
+    if split_index == 0:
+        raise ValueError("No split index found.")
+
+    train_and_validation_df = df_processed.iloc[:split_index]
+    test_df = df_processed.iloc[split_index:]
+
+    splitted_df = b_tools.split_df_percentages(
+        train_and_validation_df, [training_percentage, validation_percentage]
+    )
+
+    train_df = splitted_df[0]
+    validation_df = splitted_df[1]
+
+    return train_df, validation_df, test_df
diff --git a/requirements.txt b/requirements.txt
index d157d87..bf549e0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,4 +5,5 @@ pandas>=2.2.2
 matplotlib>=3.9.0
 seaborn>=0.13.2
 statsmodels>=0.14.1
-optuna>=3.6.1
\ No newline at end of file
+optuna>=3.6.1
+pyarrow>=17.0.0
\ No newline at end of file
diff --git a/test/requirements.txt b/test/requirements.txt
new file mode 100644
index 0000000..c714a35
--- /dev/null
+++ b/test/requirements.txt
@@ -0,0 +1 @@
+pytest>=8.x
\ No newline at end of file
diff --git a/test/test_tools.py b/test/test_tools.py
index 03f0d5d..071bd33 100644
--- a/test/test_tools.py
+++ b/test/test_tools.py
@@ -9,6 +9,8 @@
 import bibmon
 import pandas as pd
 
+import bibmon.three_w
+
 def test_complete_analysis():
     
     # load data
@@ -60,4 +62,41 @@ def test_complete_analysis():
                             fault_start = '2018-01-02 06:00:00',
                             fault_end = '2018-01-02 09:00:00') 
     
-    model.plot_importances()                                                                             
\ No newline at end of file
+    model.plot_importances()
+
+
+def test_find_df_transitions():
+    data = bibmon.load_real_data()
+
+    transitions = bibmon._bibmon_tools.find_df_transitions(data, 1, "number", "tag101")
+
+    assert transitions == [99, 101, 102, 103, 104, 106, 107, 108, 243]
+
+def test_split_df_percentages():
+    data = bibmon.load_real_data()
+
+    splitted = bibmon._bibmon_tools.split_df_percentages(data, [0.6, 0.2, 0.2])
+
+    assert splitted[0].shape[0] == 1901
+    assert splitted[1].shape[0] == 633
+    assert splitted[2].shape[0] == 633
+
+def test_split_df_percentages_error():
+    data = bibmon.load_real_data()
+
+    try:
+        _ = bibmon._bibmon_tools.split_df_percentages(data, [0.6, 0.2])
+    except ValueError:
+        assert True
+
+def test_3w_load_dataset_ini():
+    config = bibmon.three_w.tools.load_dataset_ini("dataset.ini")
+
+    assert config["VERSION"]["DATASET"] == "2.0.0"
+
+def test_3w_split_dataset():
+    data, conf = bibmon.load_3w()
+
+    train_df, validation_df, test_df = bibmon.three_w.tools.split_dataset(data, conf)
+    
+    assert (train_df.shape[0], validation_df.shape[0], test_df.shape[0]) == (85112, 21278, 136746)
\ No newline at end of file