diff --git a/README.rst b/README.rst
index b4bc297..4ccf67e 100644
--- a/README.rst
+++ b/README.rst
@@ -31,12 +31,11 @@ Time Series Forecasting Model
 
 Giotto-time provide the GAR class (Generalize Auto Regressive model). It operates in a similar way to the standard AR, but with an arbitrary number of features and with an arbitrary underlying regression model.
 
-.. image:: https://storage.googleapis.com/l2f-open-models/giotto-time/images/gar.png
-   :height: 100px
-   :width: 200 px
-   :scale: 50 %
-   :alt: alternate text
-   :align: center
+.. raw:: html
+
+   <p align="center">
+     <img width="460" src="https://storage.googleapis.com/l2f-open-models/giotto-time/images/gar.png">
+   </p>
 
 This model allows the full force of machine learning regressors (compatible with the fit-transform framework ok scikit-learn) to be combined with advanced feature creation stratagies to forecast time series in a convienent api.
 
@@ -52,6 +51,7 @@ This model allows the full force of machine learning regressors (compatible with
 >>>     horizon=4,
 >>>     features = [ShiftFeature(1), ShiftFeature(2), MovingAverageFeature(5)]
 >>> )
+>>>
 >>> train_test_splitter = TrainTestSplitter()
 >>> time_series_model = GAR(base_model=LinearRegressor())
 >>> 
@@ -67,6 +67,15 @@ Time Series Preparation
 
 To transform an input array-like structure into a DataFrame with a PeriodIndex we provide the classes:
 
+To transform an input array-like structure into a DataFrame with a PeriodIndex we provide the classes:
+
+* TimeSeriesPreparation
+* TimeSeriesConversion
+* SequenceToTimeIndexSeries
+* PandasSeriesToTimeIndexSeries
+* TimeIndexSeriesToPeriodIndexSeries
+
+
 Feature Creation
 ================
 
@@ -84,4 +93,48 @@ These features all have a scikit-learn-like interface and behave as transformers
 
 The class FeatureCreation wraps a list of features together and returns the X and y matrices from a time series given as input.
 
+Time Series Trend Model
+=======================
+
+We provide main classes to analyze and remove trends from time series in order to create trend stationary time series.
+
+Specifically, giotto-time includes ExponentialTrend, PolynomialTrend model classes and de-trending transformers.
+
+>>> import numpy as np
+>>> import pandas as pd
+>>>
+>>> import matplotlib.pyplot as plt
+>>>
+>>> from giottotime.models.regressors.linear_regressor import LinearRegressor
+>>> from giottotime.loss_functions.loss_functions import max_error, smape
+>>>
+>>> from giottotime.models.trend_models.polynomial_trend import PolynomialTrend
+>>>
+>>> from math import pi
+>>>
+>>> d = pd.read_csv('trend.csv', index_col=0, parse_dates=True)
+>>> tm = PolynomialTrend(order=3)
+>>>
+>>> tm.fit(d)
+>>>
+>>> d.plot(figsize=(10, 10))
+>>> plt.show()
+>>>
+>>> detrended = tm.transform(d)
+>>>
+>>> detrended.plot(figsize=(10, 10))
+>>> plt.show()
+
+.. raw:: html
+
+   <table style="width:100%">
+     <tr>
+       <th><img width="460" src="https://storage.googleapis.com/l2f-open-models/giotto-time/images/trend.png"></th>
+       <th><img width="460" src="https://storage.googleapis.com/l2f-open-models/giotto-time/images/no_trend.png"></th> 
+     </tr>
+   </table>
+
+Before the detrending tranformer, a clear quadratic trend is present in the data. For additional information on trend stationarity, see: Trend stationarity: Wikipedia - https://en.wikipedia.org/wiki/Trend_stationary.
+
+