diff --git a/benchmarks.py b/benchmarks.py
index b92776c..a40368f 100644
--- a/benchmarks.py
+++ b/benchmarks.py
@@ -13,18 +13,20 @@ def Teste(par):
 	plt.plot(x,y)
 
 # Erro quadrático médio
-def rmse(forecastions,targets):
-    return np.sqrt(np.mean((forecastions-targets)**2))
+def rmse(forecasts,targets):
+    return np.sqrt(np.nanmean((forecasts-targets)**2))
 
 # Erro Percentual médio
-def mape(forecastions,targets):
-    return np.mean(abs(forecastions-targets)/forecastions)
+def mape(forecasts,targets):
+    return np.mean(abs(forecasts-targets)/forecasts)
     
 def plotComparedSeries(original,fts,title):
 	fig = plt.figure(figsize=[20,6])
 	ax = fig.add_subplot(111)
-	forecasted = [fts.forecast(xx) for xx in original]
-	error = rmse(original,forecasted)
+	forecasted = fts.forecast(original)
+	#error = rmse(original[1:],forecasted[0:-1])
+	forecasted.insert(0,None)
+	#np.append(original,[None])
 	ax.plot(original,color='b',label="Original")
 	ax.plot(forecasted,color='r',label="Predicted")
 	handles0, labels0 = ax.get_legend_handles_labels()
diff --git a/chen.py b/chen.py
index 07615ec..1eae509 100644
--- a/chen.py
+++ b/chen.py
@@ -48,7 +48,7 @@ class ConventionalFTS(fts.FTS):
 		
 		ret = []
 		
-		for k in np.arange(1,l):
+		for k in np.arange(0,l):
 			
 			mv = common.fuzzyInstance(ndata[k], self.sets)
 		
diff --git a/ismailefendi.py b/ismailefendi.py
index 0474a40..8787ae2 100644
--- a/ismailefendi.py
+++ b/ismailefendi.py
@@ -64,7 +64,7 @@ class ImprovedWeightedFTS(fts.FTS):
 		
 		ret = []
 		
-		for k in np.arange(1,l):
+		for k in np.arange(0,l):
 			
 			mv = common.fuzzyInstance(ndata[k], self.sets)
 		
diff --git a/sadaei.py b/sadaei.py
index b18fc7e..15ae6e3 100644
--- a/sadaei.py
+++ b/sadaei.py
@@ -61,7 +61,7 @@ class ExponentialyWeightedFTS(fts.FTS):
 		
 		ret = []
 		
-		for k in np.arange(1,l):
+		for k in np.arange(0,l):
 			
 			mv = common.fuzzyInstance(ndata[k], self.sets)
 		
diff --git a/yu.py b/yu.py
index 74d2d64..58ae51a 100644
--- a/yu.py
+++ b/yu.py
@@ -57,7 +57,7 @@ class WeightedFTS(fts.FTS):
 		
 		ret = []
 		
-		for k in np.arange(1,l):
+		for k in np.arange(0,l):
 			
 			mv = common.fuzzyInstance(ndata[k], self.sets)