diff --git a/python/fbprophet/diagnostics.py b/python/fbprophet/diagnostics.py
index 40fc38d..62c5ce1 100644
--- a/python/fbprophet/diagnostics.py
+++ b/python/fbprophet/diagnostics.py
@@ -301,7 +301,7 @@ def prophet_copy(m, cutoff=None):
     return m2
 
 
-def performance_metrics(df, metrics=None, rolling_window=0.1):
+def performance_metrics(df, metrics=None, rolling_window=0.1, monthly=False):
     """Compute performance metrics from cross-validation results.
 
     Computes a suite of performance metrics on the output of cross-validation.
@@ -311,6 +311,7 @@ def performance_metrics(df, metrics=None, rolling_window=0.1):
     'mae': mean absolute error
     'mape': mean absolute percent error
     'mdape': median absolute percent error
+    'smape': symmetric mean absolute percentage error
     'coverage': coverage of the upper and lower intervals
 
     A subset of these can be specified by passing a list of names as the
@@ -337,15 +338,17 @@ def performance_metrics(df, metrics=None, rolling_window=0.1):
     ----------
     df: The dataframe returned by cross_validation.
     metrics: A list of performance metrics to compute. If not provided, will
-        use ['mse', 'rmse', 'mae', 'mape', 'mdape', 'coverage'].
+        use ['mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'coverage'].
     rolling_window: Proportion of data to use in each rolling window for
-        computing the metrics. Should be in [0, 1] to average
+        computing the metrics. Should be in [0, 1] to average.
+    monthly: monthly=True will compute horizons as numbers of calendar months 
+        from the cutoff date, starting from 0 for the cutoff month.
 
     Returns
     -------
     Dataframe with a column for each metric, and column 'horizon'
     """
-    valid_metrics = ['mse', 'rmse', 'mae', 'mape', 'mdape', 'coverage']
+    valid_metrics = ['mse', 'rmse', 'mae', 'mape', 'mdape', 'smape', 'coverage']
     if metrics is None:
         metrics = valid_metrics
     if ('yhat_lower' not in df or 'yhat_upper' not in df) and ('coverage' in metrics):
@@ -357,7 +360,10 @@ def performance_metrics(df, metrics=None, rolling_window=0.1):
             'Valid values for metrics are: {}'.format(valid_metrics)
         )
     df_m = df.copy()
-    df_m['horizon'] = df_m['ds'] - df_m['cutoff']
+    if monthly:
+        df_m['horizon'] = df_m['ds'].dt.to_period('M').astype(int) - df_m['cutoff'].dt.to_period('M').astype(int)
+    else:
+        df_m['horizon'] = df_m['ds'] - df_m['cutoff']
     df_m.sort_values('horizon', inplace=True)
     if 'mape' in metrics and df_m['y'].abs().min() < 1e-8:
         logger.info('Skipping MAPE because y close to 0')
@@ -590,6 +596,7 @@ def mdape(df, w):
 
 def smape(df, w):
     """Symmetric mean absolute percentage error
+    based on Chen and Yang (2004) formula
 
     Parameters
     ----------
@@ -600,7 +607,7 @@ def smape(df, w):
     -------
     Dataframe with columns horizon and smape.
     """
-    sape = np.abs(df['yhat']-df['y']) / ((np.abs(df['y']) + np.abs(df['yhat'])) /2)
+    sape = np.abs(df['y'] - df['yhat']) / ((np.abs(df['y']) + np.abs(df['yhat'])) / 2)
     if w < 0:
         return pd.DataFrame({'horizon': df['horizon'], 'smape': sape})
     return rolling_mean_by_h(
diff --git a/python/fbprophet/tests/test_diagnostics.py b/python/fbprophet/tests/test_diagnostics.py
index 8284a93..d2d9f17 100644
--- a/python/fbprophet/tests/test_diagnostics.py
+++ b/python/fbprophet/tests/test_diagnostics.py
@@ -190,7 +190,7 @@ class TestDiagnostics(TestCase):
         df_none = diagnostics.performance_metrics(df_cv, rolling_window=-1)
         self.assertEqual(
             set(df_none.columns),
-            {'horizon', 'coverage', 'mae', 'mape', 'mdape', 'mse', 'rmse'},
+            {'horizon', 'coverage', 'mae', 'mape', 'mdape', 'mse', 'rmse', 'smape'},
         )
         self.assertEqual(df_none.shape[0], 16)
         # Aggregation level 0