diff --git a/benchmarks/arima.py b/benchmarks/arima.py
index a5e1817..7e7162a 100644
--- a/benchmarks/arima.py
+++ b/benchmarks/arima.py
@@ -33,7 +33,7 @@ class ARIMA(fts.FTS):
         self.d = order[1]
         self.q = order[2]
         self.order = self.p + self.q
-        self.shortname = "ARIMA(" + str(self.p) + "," + str(self.d) + "," + str(self.q) + ")"
+        self.shortname = "ARIMA(" + str(self.p) + "," + str(self.d) + "," + str(self.q) + ") - " + str(self.alpha)
 
         old_fit = self.model_fit
         try:
diff --git a/benchmarks/quantreg.py b/benchmarks/quantreg.py
index 88d3570..57d6090 100644
--- a/benchmarks/quantreg.py
+++ b/benchmarks/quantreg.py
@@ -41,7 +41,7 @@ class QuantileRegression(fts.FTS):
             self.upper_qt = [k for k in uqt.params]
             self.lower_qt = [k for k in lqt.params]
 
-        self.shortname = "QAR(" + str(self.order) + ")"
+        self.shortname = "QAR(" + str(self.order) + ") - " + str(self.alpha)
 
     def linearmodel(self,data,params):
         #return params[0] + sum([ data[k] * params[k+1] for k in np.arange(0, self.order) ])
@@ -74,7 +74,7 @@ class QuantileRegression(fts.FTS):
             sample = ndata[k - self.order: k]
             up = self.linearmodel(sample, self.upper_qt)
             down = self.linearmodel(sample, self.lower_qt)
-            ret.append([up, down])
+            ret.append([down, up])
 
         ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]], interval=True)
 
diff --git a/tests/general.py b/tests/general.py
index 3ac8ea6..ed7abac 100644
--- a/tests/general.py
+++ b/tests/general.py
@@ -54,7 +54,7 @@ nasdaq = np.array(nasdaqpd["avg"][0:5000])
 from pyFTS.benchmarks import distributed_benchmarks as bchmk
 #from pyFTS.benchmarks import parallel_benchmarks as bchmk
 from pyFTS.benchmarks import Util
-from pyFTS.benchmarks import arima, quantreg
+from pyFTS.benchmarks import arima, quantreg, Measures
 
 #Util.cast_dataframe_to_synthetic_point("experiments/taiex_point_analitic.csv","experiments/taiex_point_sintetic.csv",11)
 
@@ -64,13 +64,18 @@ from pyFTS.benchmarks import arima, quantreg
 #tmp.train(taiex[:1600], None, order=(2,0,2))
 #teste = tmp.forecastInterval(taiex[1600:1605])
 
-#tmp = quan#treg.QuantileRegression("")
-#tmp.train(taiex[:1600], None, order=2)
-#teste = tmp.forecast(taiex[1600:1605])
+"""
+tmp = quantreg.QuantileRegression("", alpha=0.25)
+tmp.train(taiex[:1600], None, order=1)
+teste = tmp.forecastInterval(taiex[1600:1605])
 
-#print(taiex[1600:1605])
-#print(teste)
+print(taiex[1600:1605])
+print(teste)
 
+kk = Measures.get_interval_statistics(taiex[1600:1605], tmp)
+
+print(kk)
+"""
 #bchmk.teste(taiex,['192.168.0.109', '192.168.0.101'])
 
 diff = Transformations.Differential(1)
@@ -89,6 +94,7 @@ bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4, #models=[yu.Weighted
                      partitions= np.arange(3,20,step=2), #transformation=diff,
                      dump=True, save=True, file="experiments/sondaws_point_analytic_diff.csv",
                      nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
+"""
 #"""
 
 bchmk.interval_sliding_window(nasdaq, 2000, train=0.8, inc=0.1,#models=[yu.WeightedFTS], # #
@@ -105,6 +111,8 @@ bchmk.interval_sliding_window(nasdaq, 2000, train=0.8, inc=0.1, #models=[yu.Weig
                      dump=True, save=True, file="experiments/nasdaq_interval_analytic_diff.csv",
                      nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
 
+#"""
+
 """
 from pyFTS.partitioners import Grid
 from pyFTS import pwfts
@@ -123,4 +131,4 @@ x = tmp.forecastInterval(taiex[1600:1610])
 
 print(taiex[1600:1610])
 print(x)
-"""
\ No newline at end of file
+#"""
\ No newline at end of file