Seasonality values can be negative while prediction stays positive

Author: mrtergl

python/prophet/forecaster.py

@@ -1326,6 +1326,10 @@ def predict(self, df: pd.DataFrame = None, vectorized: bool = True) -> pd.DataFr
                 df2['trend'] * (1 + df2['multiplicative_terms'])
                 + df2['additive_terms']
         )
+
+        if not self.negative_prediction_values:
+            df2['yhat'] = df2['yhat'].clip(lower=0)
+
         return df2
 
     @staticmethod

python/prophet/tests/test_prophet.py

@@ -434,21 +434,15 @@ def test_override_n_changepoints(self, daily_univariate_ts, backend):
         cp = m.changepoints_t
         assert cp.shape[0] == 15
 
-    @pytest.mark.parametrize(
-        "expected",
-        [5.656087514685135],
-    )
-    def test_without_negative_predictions(self, subdaily_univariate_ts, backend, expected):
+    def test_without_negative_predictions(self, subdaily_univariate_ts, backend):
         test_days = 280
         train, test = train_test_split(subdaily_univariate_ts, test_days)
-        forecaster = Prophet(stan_backend=backend, negative_prediction_values=False)
+        forecaster = Prophet(stan_backend=backend, negative_prediction_values=False, weekly_seasonality=True, yearly_seasonality=True)
         forecaster.fit(train, seed=1237861298)
         np.random.seed(876543987)
         future = forecaster.make_future_dataframe(test_days, include_history=False)
         future = forecaster.predict(future)
-        res = rmse(future["yhat"], test["y"])
-        tolerance = 1e-5
-        assert res == pytest.approx(expected, rel=tolerance), "backend: {}".format(forecaster.stan_backend)
+        assert (future['yhat'].values >= 0).all()
 
 
 class TestProphetSeasonalComponent: