diff --git a/pyFTS/benchmarks/quantreg.py b/pyFTS/benchmarks/quantreg.py
index 91c7cb6..a2d5bcd 100644
--- a/pyFTS/benchmarks/quantreg.py
+++ b/pyFTS/benchmarks/quantreg.py
@@ -97,7 +97,7 @@ class QuantileRegression(fts.FTS):
 
     def forecast_ahead_interval(self, ndata, steps, **kwargs):
 
-        smoothing = kwargs.get("smoothing", 0.1)
+        smoothing = kwargs.get("smoothing", 0.01)
 
         l = len(ndata)
 
@@ -113,7 +113,7 @@ class QuantileRegression(fts.FTS):
 
             tmpk = k-self.order
 
-            ret.append([intl[0]*(1 + (tmpk*smoothing)), intl[1]*(1 + (tmpk*smoothing))])
+            ret.append([intl[0]*(1 - (tmpk*smoothing)), intl[1]*(1 + (tmpk*smoothing))])
 
         return ret[-steps:]
 
@@ -139,7 +139,7 @@ class QuantileRegression(fts.FTS):
         return ret
 
     def forecast_ahead_distribution(self, ndata, steps, **kwargs):
-        smoothing = kwargs.get("smoothing", 0.1)
+        smoothing = kwargs.get("smoothing", 0.01)
 
         l = len(ndata)
 
@@ -158,7 +158,7 @@ class QuantileRegression(fts.FTS):
             for qt in self.dist_qt:
                 intl1 = self.point_to_interval(nmeans[k - self.order: k], qt[0], qt[1])
                 tmpk = k - self.order
-                intl2 = [intl1[0] * (1 + (tmpk * smoothing)), intl1[1] * (1 + (tmpk * smoothing))]
+                intl2 = [intl1[0] * (1 - (tmpk * smoothing)), intl1[1] * (1 + (tmpk * smoothing))]
                 intervals.append(intl2)
             dist.append_interval(intervals)
 
diff --git a/pyFTS/common/Util.py b/pyFTS/common/Util.py
index 8159d8d..5f48c39 100644
--- a/pyFTS/common/Util.py
+++ b/pyFTS/common/Util.py
@@ -177,8 +177,6 @@ def plot_distribution2(probabilitydist, data, **kwargs):
     import matplotlib.colorbar as cbar
     import matplotlib.cm as cm
 
-    order = kwargs.get('order', 1)
-
     ax = kwargs.get('ax',None)
     if ax is None:
         fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15, 5])
@@ -188,7 +186,7 @@ def plot_distribution2(probabilitydist, data, **kwargs):
     cmap = kwargs.get('cmap','Blues')
     cmap = plt.get_cmap(cmap)
 
-    start_at = kwargs.get('start_at',0) + order - 1
+    start_at = kwargs.get('start_at',0)
 
     x = [k + start_at for k in range(l + 1)]
 
@@ -275,7 +273,7 @@ def plot_interval2(intervals, data, **kwargs):
     if ax is None:
         fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15, 5])
 
-    for k in np.arange(0, start_at-1):
+    for k in np.arange(0, start_at):
         intervals.insert(0, [None,None])
 
     intervals.insert(start_at, [data[start_at], data[start_at]])
diff --git a/pyFTS/models/ensemble/ensemble.py b/pyFTS/models/ensemble/ensemble.py
index c429ae3..0e26f05 100644
--- a/pyFTS/models/ensemble/ensemble.py
+++ b/pyFTS/models/ensemble/ensemble.py
@@ -303,6 +303,7 @@ class SimpleEnsembleFTS(EnsembleFTS):
         """Possible variations of number of partitions on internal models"""
         self.orders = kwargs.get('orders', [1,2,3])
         """Possible variations of order on internal models"""
+        self.uod_clip = False
 
     def train(self, data, **kwargs):
         for k in self.partitions:
diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py
index 1dd4c14..971e76f 100644
--- a/pyFTS/tests/general.py
+++ b/pyFTS/tests/general.py
@@ -12,6 +12,7 @@ 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
+from pyFTS.models.ensemble import ensemble
 from pyFTS.common import Transformations, Membership
 from pyFTS.benchmarks import arima, quantreg, BSTS
 from pyFTS.fcm import fts, common, GA
@@ -20,17 +21,19 @@ from pyFTS.data import Enrollments, TAIEX
 
 data = TAIEX.get_data()
 
-train = data[:500]
-test = data[500:1000]
+train = data[:800]
+test = data[800:1000]
 
-model = quantreg.QuantileRegression(order=1, dist=True)
+model = ensemble.SimpleEnsembleFTS(fts_method=hofts.HighOrderFTS)
+#model = quantreg.QuantileRegression(order=2, dist=True)
+#model = arima.ARIMA(order = (2,0,0))
 model.fit(train)
 
 horizon=5
 
 #points  = model.predict(test[:10], type='point', steps_ahead=horizon)
 
-intervals = model.predict(test[:10], type='interval', alpha=.05, smoothing=0.01, steps_ahead=horizon)
+intervals = model.predict(test[:10], type='interval', alpha=.05,  steps_ahead=horizon)
 print(test[:10])
 print(intervals)
 distributions = model.predict(test[:10], type='distribution', steps_ahead=horizon, num_bins=100)
@@ -39,8 +42,10 @@ distributions = model.predict(test[:10], type='distribution', steps_ahead=horizo
 fig, ax = plt.subplots(nrows=1, ncols=1,figsize=[15,5])
 
 ax.plot(test[:10], label='Original',color='black')
-cUtil.plot_interval2(intervals, test[:10], start_at=3, ax=ax)
-cUtil.plot_distribution2(distributions, test[:10], start_at=2, ax=ax, cmap="Blues")
+cUtil.plot_interval2(intervals, test[:10], start_at=model.order, ax=ax)
+cUtil.plot_distribution2(distributions, test[:10], start_at=model.order, ax=ax, cmap="Blues")
+
+print("")
 
 '''
 model = fts.FCM_FTS(partitioner=fs, order=1)