diff --git a/pyforecaster/forecasting_models/gradientboosters.py b/pyforecaster/forecasting_models/gradientboosters.py
index 27d16b8..522623e 100644
--- a/pyforecaster/forecasting_models/gradientboosters.py
+++ b/pyforecaster/forecasting_models/gradientboosters.py
@@ -145,8 +145,13 @@ def predict_single(self, i, x):
         return self.multi_step_model.predict(x,num_threads=1).reshape(-1, 1)
 
     def predict_parallel(self, x):
-        with concurrent.futures.ProcessPoolExecutor(max_workers=2) as executor:
-            y_hat = list(executor.map(partial(self.predict_single, x=x), np.arange(self.n_multistep)))
+        if self.parallel:
+            with concurrent.futures.ProcessPoolExecutor(max_workers=2) as executor:
+                y_hat = list(executor.map(partial(self.predict_single, x=x), np.arange(self.n_multistep)))
+        else:
+            y_hat = []
+            for i in range(self.n_multistep):
+                y_hat.append(self.predict_single(i, x))
         return np.hstack(y_hat)
 
     @staticmethod
diff --git a/tests/test_boosters.py b/tests/test_boosters.py
index bb61ebd..62771e0 100644
--- a/tests/test_boosters.py
+++ b/tests/test_boosters.py
@@ -32,8 +32,19 @@ def test_linear_val_split(self):
         y_hat_lin = m_lin.predict(x_te)
         q = m_lin.predict_quantiles(x_te)
 
-        m_lgbhybrid = LGBMHybrid(red_frac_multistep=0.1,  val_ratio=0.3, lgb_pars={'num_leaves': 300, 'n_estimators': 10, 'learning_rate':0.05},
-                                 n_single=10, parallel=True, formatter=formatter, metadata_features=['minuteofday', 'utc_offset', 'dayofweek', 'hour'],tol_period='1h', keep_last_seconds=3600).fit(x_tr, y_tr)
+        m_lgbhybrid = LGBMHybrid(red_frac_multistep=0.1, val_ratio=0.3,
+                                 lgb_pars={'num_leaves': 300, 'n_estimators': 10, 'learning_rate': 0.05},
+                                 n_single=10, parallel=False, formatter=formatter,
+                                 metadata_features=['minuteofday', 'utc_offset', 'dayofweek', 'hour'], tol_period='1h',
+                                 keep_last_seconds=3600).fit(x_tr, y_tr)
+        y_hat_lgbh = m_lgbhybrid.predict(x_te)
+        q = m_lgbhybrid.predict_quantiles(x_te)
+
+        m_lgbhybrid = LGBMHybrid(red_frac_multistep=0.1, val_ratio=0.3,
+                                 lgb_pars={'num_leaves': 300, 'n_estimators': 10, 'learning_rate': 0.05},
+                                 n_single=10, parallel=True, formatter=formatter,
+                                 metadata_features=['minuteofday', 'utc_offset', 'dayofweek', 'hour'], tol_period='1h',
+                                 keep_last_seconds=3600).fit(x_tr, y_tr)
         y_hat_lgbh = m_lgbhybrid.predict(x_te)
         q = m_lgbhybrid.predict_quantiles(x_te)