diff --git a/pyFTS/models/nonstationary/util.py b/pyFTS/models/nonstationary/util.py
index 7da2a95..69a29c6 100644
--- a/pyFTS/models/nonstationary/util.py
+++ b/pyFTS/models/nonstationary/util.py
@@ -63,7 +63,7 @@ def plot_sets_conditional(model, data, step=1, size=[5, 5], colors=None,
 
     for t in range:
         model.forecast([data[t]])
-        perturb = model.perturbation_factors(data[t])
+        perturb = model.conditional_perturbation_factors(data[t])
 
         for ct, key in enumerate(model.partitioner.ordered_sets):
             set = model.partitioner.sets[key]
diff --git a/pyFTS/tests/nonstationary.py b/pyFTS/tests/nonstationary.py
index ae038c7..6c71bd9 100644
--- a/pyFTS/tests/nonstationary.py
+++ b/pyFTS/tests/nonstationary.py
@@ -11,7 +11,7 @@ import pandas as pd
 from pyFTS.data import TAIEX, NASDAQ, SP500, artificial, mackey_glass
 
 mackey_glass.get_data()
-'''
+
 datasets = {
     "TAIEX": TAIEX.get_data()[:4000],
     "SP500": SP500.get_data()[10000:14000],
@@ -53,7 +53,38 @@ partitions = {'CMIV': {'BoxCox(0)': 36, 'Differential(1)': 11, 'None': 8},
  'SP500': {'BoxCox(0)': 33, 'Differential(1)': 7, 'None': 33},
  'TAIEX': {'BoxCox(0)': 39, 'Differential(1)': 31, 'None': 33}}
 
+from pyFTS.models.nonstationary import partitioners as nspart, cvfts, util as nsUtil
 
+
+def model_details(ds, tf, train_split, test_split):
+    data = datasets[ds]
+    train = data[:train_split]
+    test = data[train_split:test_split]
+    transformation = transformations[tf]
+    fs = nspart.simplenonstationary_gridpartitioner_builder(data=train, npart=partitions[ds][tf],
+                                                            transformation=transformation)
+    model = nsfts.NonStationaryFTS(partitioner=fs)
+    model.fit(train)
+    print(model)
+    forecasts = model.predict(test)
+    residuals = np.array(test[1:]) - np.array(forecasts[:-1])
+
+    fig, axes = plt.subplots(nrows=2, ncols=1, figsize=[15, 10])
+
+    axes[0].plot(test[1:], label="Original")
+    axes[0].plot(forecasts[:-1], label="Forecasts")
+
+    axes[1].set_title("Residuals")
+    axes[1].plot(residuals)
+    handles0, labels0 = axes[0].get_legend_handles_labels()
+    lgd = axes[0].legend(handles0, labels0, loc=2)
+
+    nsUtil.plot_sets_conditional(model, test, step=10, size=[12, 5])
+
+model_details('NASDAQ','None',200,2000)
+
+
+'''
 tag = 'benchmarks'