diff --git a/benchmarks/arima.py b/benchmarks/arima.py
index 0c40b13..437f642 100644
--- a/benchmarks/arima.py
+++ b/benchmarks/arima.py
@@ -24,11 +24,11 @@ class ARIMA(fts.FTS):
         self.benchmark_only = True
         self.min_order = 1
 
-    def train(self, data, sets, order=(2,1,1), parameters=None):
+    def train(self, data, sets, order, parameters=None):
         self.p = order[0]
         self.d = order[1]
         self.q = order[2]
-        self.order = max([self.p, self.d, self.q])
+        self.order = self.p + self.q
         self.shortname = "ARIMA(" + str(self.p) + "," + str(self.d) + "," + str(self.q) + ")"
 
         old_fit = self.model_fit
@@ -51,13 +51,19 @@ class ARIMA(fts.FTS):
 
         ret = []
 
-        ar = np.array([self.ar(ndata[k - self.p: k]) for k in np.arange(self.p, l)])
+        if self.d == 0:
+            ar = np.array([self.ar(ndata[k - self.p: k]) for k in np.arange(self.p, l)])
+        else:
+            ar = np.array([ndata[k] + self.ar(ndata[k - self.p: k]) for k in np.arange(self.p, l)])
 
-        residuals = np.array([ar[k - self.p] - ndata[k] for k in np.arange(self.p, l)])
+        if self.q > 0:
+            residuals = np.array([ndata[k] - ar[k - self.p] for k in np.arange(self.p, l)])
 
-        ma = np.array([self.ma(residuals[k - self.q: k]) for k in np.arange(self.q, len(ar) + 1)])
+            ma = np.array([self.ma(residuals[k - self.q: k]) for k in np.arange(self.q, len(residuals))])
 
-        ret = ar + ma
+            ret = ar[self.q:] + ma
+        else:
+            ret = ar
 
         ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
 
diff --git a/benchmarks/distributed_benchmarks.py b/benchmarks/distributed_benchmarks.py
index 84cb1d0..988a1bd 100644
--- a/benchmarks/distributed_benchmarks.py
+++ b/benchmarks/distributed_benchmarks.py
@@ -101,7 +101,7 @@ def point_sliding_window(data, windowsize, train=0.8, models=None, partitioners=
                             quantreg.QuantileRegression, quantreg.QuantileRegression]
 
     if benchmark_models_parameters is None:
-        benchmark_models_parameters = [None, (1, 0, 1), (1, 1, 1), (2, 1, 1), (2, 1, 2), 1, 2]
+        benchmark_models_parameters = [None, (1, 0, 0), (1, 0, 1), (2, 0, 1), (2, 0, 2), 1, 2]
 
     cluster = dispy.JobCluster(run_point, nodes=nodes) #, depends=dependencies)
 
diff --git a/tests/general.py b/tests/general.py
index 797a9fb..3881cf1 100644
--- a/tests/general.py
+++ b/tests/general.py
@@ -64,9 +64,14 @@ from pyFTS.benchmarks import arima, quantreg
 #Util.plot_dataframe_point("experiments/taiex_point_sintetic.csv","experiments/taiex_point_analitic.csv",11)
 
 #tmp = arima.ARIMA("")
-#tmp.train(taiex[:1600],(2,1,1))
-#teste = tmp.forecast(taiex[1600:2000])
+#tmp.train(taiex[:1600], None, order=(1,0,1))
+#teste = tmp.forecast(taiex[1600:1610])
 
+#tmp = quantreg.QuantileRegression("")
+#tmp.train(taiex[:1600], None, order=1)
+#teste = tmp.forecast(taiex[1600:1610])
+
+#print(taiex[1600:1610])
 #print(teste)
 
 #bchmk.teste(taiex,['192.168.0.109', '192.168.0.101'])
@@ -75,7 +80,7 @@ bchmk.point_sliding_window(taiex,2000,train=0.8, #models=[yu.WeightedFTS], # #
                      partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
                      partitions= np.arange(10,200,step=10), #transformation=diff,
                      dump=True, save=True, file="experiments/taiex_point_analytic.csv",
-                     nodes=['192.168.0.102', '192.168.0.109']) #, depends=[hofts, ifts])
+                     nodes=['192.168.0.102', '192.168.0.109', '192.168.0.106']) #, depends=[hofts, ifts])
 
 #bchmk.testa(taiex,[10,20],partitioners=[Grid.GridPartitioner], nodes=['192.168.0.109', '192.168.0.101'])