diff --git a/pyFTS/common/fts.py b/pyFTS/common/fts.py
index f500641..6650858 100644
--- a/pyFTS/common/fts.py
+++ b/pyFTS/common/fts.py
@@ -91,6 +91,12 @@ class FTS(object):
 
         return best
 
+    def clip_uod(self, ndata):
+        if self.uod_clip:
+            ndata = np.clip(ndata, self.original_min, self.original_max)
+        return ndata
+
+
     def predict(self, data, **kwargs):
         """
         Forecast using trained model
@@ -116,8 +122,7 @@ class FTS(object):
         else:
             ndata = self.apply_transformations(data)
 
-            if self.uod_clip:
-                ndata = np.clip(ndata, self.original_min, self.original_max)
+        ndata = self.clip_uod(ndata)
 
         if 'distributed' in kwargs:
             distributed = kwargs.pop('distributed')
diff --git a/pyFTS/models/ifts.py b/pyFTS/models/ifts.py
index ae85ae0..ee59292 100644
--- a/pyFTS/models/ifts.py
+++ b/pyFTS/models/ifts.py
@@ -52,17 +52,16 @@ class IntervalFTS(hofts.HighOrderFTS):
         mb = [fuzzySets[k].membership(data[k]) for k in np.arange(0, len(data))]
         return mb
 
-
     def forecast_interval(self, ndata, **kwargs):
 
         ret = []
 
         l = len(ndata)
 
-        if l <= self.order:
+        if l < self.order:
             return ndata
 
-        for k in np.arange(self.max_lag, l):
+        for k in np.arange(self.max_lag, l+1):
 
             sample = ndata[k - self.max_lag: k]
 
@@ -88,6 +87,16 @@ class IntervalFTS(hofts.HighOrderFTS):
 
         return ret
 
+    def forecast_ahead_interval(self, data, steps, **kwargs):
+        ret = [[x, x] for x in data[:self.max_lag]]
+        for k in np.arange(self.max_lag, self.max_lag + steps):
+            interval_lower = self.clip_uod(self.forecast_interval([x[0] for x in ret[k - self.max_lag: k]])[0])
+            interval_upper = self.clip_uod(self.forecast_interval([x[1] for x in ret[k - self.max_lag: k]])[0])
+            interval = [np.nanmin(interval_lower), np.nanmax(interval_upper)]
+            ret.append(interval)
+
+        return ret[-steps:]
+
 
 class WeightedIntervalFTS(hofts.WeightedHighOrderFTS):
     """
@@ -128,17 +137,15 @@ class WeightedIntervalFTS(hofts.WeightedHighOrderFTS):
         mb = [fuzzySets[k].membership(data[k]) for k in np.arange(0, len(data))]
         return mb
 
-
     def forecast_interval(self, ndata, **kwargs):
-
         ret = []
 
         l = len(ndata)
 
-        if l <= self.order:
+        if l < self.order:
             return ndata
 
-        for k in np.arange(self.max_lag, l):
+        for k in np.arange(self.max_lag, l+1):
 
             sample = ndata[k - self.max_lag: k]
 
@@ -163,3 +170,15 @@ class WeightedIntervalFTS(hofts.WeightedHighOrderFTS):
             ret.append([lo_, up_])
 
         return ret
+
+    def forecast_ahead_interval(self, data, steps, **kwargs):
+        ret = [[x, x] for x in data[:self.max_lag]]
+        for k in np.arange(self.max_lag, self.max_lag + steps):
+            interval_lower = self.clip_uod(self.forecast_interval([x[0] for x in ret[k - self.max_lag: k]])[0])
+            interval_upper = self.clip_uod(self.forecast_interval([x[1] for x in ret[k - self.max_lag: k]])[0])
+            interval = [np.nanmin(interval_lower), np.nanmax(interval_upper)]
+            ret.append(interval)
+
+        return ret[-steps:]
+
+
diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py
index 2b0456a..091e2b6 100644
--- a/pyFTS/tests/general.py
+++ b/pyFTS/tests/general.py
@@ -61,18 +61,19 @@ methods_parameters = [
     {'order':2 }
 ]
 
-for dataset_name, dataset in datasets.items():
-    bchmk.sliding_window_benchmarks2(dataset, 1000, train=0.8, inc=0.2,
-                                     benchmark_models=True,
-                                     benchmark_methods=methods,
-                                     benchmark_methods_parameters=methods_parameters,
-                                     methods=[],
-                                     methods_parameters=[],
-                                     transformations=[None],
-                                     orders=[3],
-                                     steps_ahead=[10],
-                                     partitions=[None],
-                                     type='interval',
-                                     #distributed=True, nodes=['192.168.0.110', '192.168.0.107','192.168.0.106'],
-                                     file="tmp.db", dataset=dataset_name, tag="experiments")
+#for dataset_name, dataset in datasets.items():
+bchmk.sliding_window_benchmarks2(TAIEX.get_data()[:5000], 1000, train=0.8, inc=0.2,
+                                 benchmark_models=False,
+                                 benchmark_methods=methods,
+                                 benchmark_methods_parameters=methods_parameters,
+                                 methods=[ifts.IntervalFTS, ifts.WeightedIntervalFTS],
+                                 methods_parameters=[{},{}],
+                                 transformations=[None],
+                                 orders=[1,2,3],
+                                 steps_ahead=[10],
+                                 partitions=[33],
+                                 type='interval',
+                                 #distributed=True, nodes=['192.168.0.110', '192.168.0.107','192.168.0.106'],
+                                 #file="tmp.db", dataset=dataset_name, tag="experiments")
+                                file="tmp.db", dataset='TAIEX', tag="experiments")
 #'''
\ No newline at end of file