diff --git a/pyFTS/benchmarks/Measures.py b/pyFTS/benchmarks/Measures.py
index 7cdd07a..979a9cb 100644
--- a/pyFTS/benchmarks/Measures.py
+++ b/pyFTS/benchmarks/Measures.py
@@ -334,6 +334,8 @@ def get_point_statistics(data, model, **kwargs):
 
         nforecasts = np.array(forecasts[:-1])
 
+        print(model.shortname)
+
         ret.append(np.round(rmse(ndata[model.max_lag:], nforecasts), 2))
         ret.append(np.round(mape(ndata[model.max_lag:], nforecasts), 2))
         ret.append(np.round(UStatistic(ndata[model.max_lag:], nforecasts), 2))
diff --git a/pyFTS/benchmarks/arima.py b/pyFTS/benchmarks/arima.py
index b524f94..3980310 100644
--- a/pyFTS/benchmarks/arima.py
+++ b/pyFTS/benchmarks/arima.py
@@ -39,6 +39,7 @@ class ARIMA(fts.FTS):
             self.d = order[1]
             self.q = order[2]
             self.order = self.p + self.q + (self.q - 1 if self.q > 0 else 0)
+            self.max_lag = self.order
             self.d = len(self.transformations)
             self.shortname = "ARIMA(" + str(self.p) + "," + str(self.d) + "," + str(self.q) + ") - " + str(self.alpha)
 
diff --git a/pyFTS/benchmarks/benchmarks.py b/pyFTS/benchmarks/benchmarks.py
index 0b3e20b..53692d2 100644
--- a/pyFTS/benchmarks/benchmarks.py
+++ b/pyFTS/benchmarks/benchmarks.py
@@ -104,6 +104,8 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs):
     :keyword
         benchmark_methods:  a list with Non FTS models to benchmark. The default is None.
         benchmark_methods_parameters:  a list with Non FTS models parameters. The default is None.
+        benchmark_models: A boolean value indicating if external FTS methods will be used on benchmark. The default is False.
+        build_methods: A boolean value indicating if the default FTS methods will be used on benchmark. The default is True.
         dataset: the dataset name to identify the current set of benchmarks results on database.
         distributed: A boolean value indicating if the forecasting procedure will be distributed in a dispy cluster. . The default is False
         file: file path to save the results. The default is benchmarks.db.
@@ -146,7 +148,7 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs):
 
     pool = [] if models is None else models
 
-    if models is None and methods is None:
+    if methods is None:
         if type  == 'point':
             methods = get_point_methods()
         elif type == 'interval':
@@ -154,7 +156,9 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs):
         elif type == 'distribution':
             methods = get_probabilistic_methods()
 
-    if models is None:
+    build_methods = __pop("build_methods", True, kwargs)
+
+    if build_methods:
         for method in methods:
             mfts = method()