diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle
index 8610d30..61ba8e8 100644
Binary files a/docs/build/doctrees/environment.pickle and b/docs/build/doctrees/environment.pickle differ
diff --git a/pyFTS/tests/transformations.py b/pyFTS/tests/transformations.py
new file mode 100644
index 0000000..a7049e6
--- /dev/null
+++ b/pyFTS/tests/transformations.py
@@ -0,0 +1,74 @@
+#!/usr/bin/python
+# -*- coding: utf8 -*-
+
+import os
+import numpy as np
+import matplotlib.pylab as plt
+
+import pandas as pd
+
+from pyFTS.common import Util as cUtil, FuzzySet
+from pyFTS.partitioners import Grid, Entropy, Util as pUtil, Simple
+from pyFTS.benchmarks import benchmarks as bchmk, Measures
+from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei, ifts
+from pyFTS.models.ensemble import ensemble
+from pyFTS.common import Membership, Util
+from pyFTS.benchmarks import arima, quantreg, BSTS, gaussianproc, knn
+from pyFTS.common import Transformations
+
+tdiff = Transformations.Differential(1)
+
+boxcox = Transformations.BoxCox(0)
+
+from pyFTS.data import Enrollments, AirPassengers
+
+'''
+data = AirPassengers.get_data()
+
+roi = Transformations.ROI()
+
+#plt.plot(data)
+
+_roi = roi.apply(data)
+
+#plt.plot(_roi)
+
+plt.plot(roi.inverse(_roi, data))
+'''
+
+'''
+data = AirPassengers.get_dataframe()
+data['Month'] = pd.to_datetime(data['Month'], format='%Y-%m')
+trend = Transformations.LinearTrend(data_field='Passengers', index_field='Month',
+                                    index_type='datetime', datetime_mask='%Y-%d')
+
+trend.train(data)
+
+plt.plot(data['Passengers'].values)
+
+plt.plot(trend.trend(data))
+
+detrend = trend.apply(data)
+
+plt.plot(trend.inverse(detrend, data, date_offset=pd.DateOffset(months=1)))
+'''
+
+
+data = Enrollments.get_dataframe()
+
+trend = Transformations.LinearTrend(data_field='Enrollments', index_field='Year')
+
+trend.train(data)
+
+plt.plot(data['Enrollments'].values)
+
+plt.plot(trend.trend(data)) #)
+
+detrend = trend.apply(data)
+
+plt.plot(trend.inverse(detrend, data))
+
+
+plt.show()
+
+print(data)