diff --git a/pyFTS/common/fts.py b/pyFTS/common/fts.py
index 82b602b..53d3b5b 100644
--- a/pyFTS/common/fts.py
+++ b/pyFTS/common/fts.py
@@ -458,7 +458,8 @@ class FTS(object):
             return data
 
     def get_UoD(self):
-        return [self.original_min, self.original_max]
+        #return [self.original_min, self.original_max]
+        return [self.partitioner.min, self.partitioner.max]
 
     def __str__(self):
         """String representation of the model"""
diff --git a/pyFTS/models/hofts.py b/pyFTS/models/hofts.py
index 4b2e698..33a6223 100644
--- a/pyFTS/models/hofts.py
+++ b/pyFTS/models/hofts.py
@@ -88,13 +88,13 @@ class HighOrderFTS(fts.FTS):
         self.detail = "Severiano, Silva, Sadaei and Guimarães"
         self.is_high_order = True
         self.min_order = 1
-        self.order= kwargs.get("order", 2)
+        self.order= kwargs.get("order", self.min_order)
         self.lags = kwargs.get("lags", None)
         self.configure_lags(**kwargs)
 
     def configure_lags(self, **kwargs):
         if "order" in kwargs:
-            self.order = kwargs.get("order", 2)
+            self.order = kwargs.get("order", self.min_order)
 
         if "lags" in kwargs:
             self.lags = kwargs.get("lags", None)
diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py
index 8f55561..02ef077 100644
--- a/pyFTS/tests/general.py
+++ b/pyFTS/tests/general.py
@@ -17,21 +17,22 @@ from pyFTS.common import Transformations
 tdiff = Transformations.Differential(1)
 
 
-from pyFTS.data import TAIEX, SP500, NASDAQ, Malaysia
+from pyFTS.data import TAIEX, SP500, NASDAQ, Malaysia, Enrollments
 
-dataset = Malaysia.get_data('temperature')[:1000]
+train_split = 2000
+test_length = 200
 
-p = Grid.GridPartitioner(data=dataset, npart=20)
+dataset = TAIEX.get_data()
 
-print(p)
+partitioner = Grid.GridPartitioner(data=dataset[:train_split], npart=35)
+partitioner_diff = Grid.GridPartitioner(data=dataset[:train_split], npart=5, transformation=tdiff)
 
-model = pwfts.ProbabilisticWeightedFTS(partitioner=p, order=2)
+pfts1_taiex = pwfts.ProbabilisticWeightedFTS(partitioner=partitioner)
+pfts1_taiex.fit(dataset[:train_split], save_model=True, file_path='pwfts', order=1)
+pfts1_taiex.shortname = "1st Order"
+#print(pfts1_taiex)
 
-model.fit(dataset) #[22, 22, 23, 23, 24])
-
-print(model)
-
-Measures.get_point_statistics(dataset, model)
+tmp = pfts1_taiex.predict(dataset[train_split:train_split+200], type='distribution')
 
 '''
 #dataset = SP500.get_data()[11500:16000]