BUG: Fix indexing with HoltWinters's forecast

Fix index when using int indices closes statsmodels#6549
bashtage · Jul 9, 2020 · 17c3d6a · 17c3d6a
1 parent 0538c0c
commit 17c3d6a
Show file tree

Hide file tree

Showing 2 changed files with 23 additions and 3 deletions.
diff --git a/statsmodels/tsa/holtwinters.py b/statsmodels/tsa/holtwinters.py
@@ -348,8 +348,12 @@ def forecast(self, steps=1):
         """
         try:
             freq = getattr(self.model._index, 'freq', 1)
-            start = self.model._index[-1] + freq
-            end = self.model._index[-1] + steps * freq
+            if isinstance(freq, int):
+                start = self.model._index.shape[0]
+                end = start + steps - 1
+            else:
+                start = self.model._index[-1] + freq
+                end = self.model._index[-1] + steps * freq
             return self.model.predict(self.params, start=start, end=end)
         except (AttributeError, ValueError):
             # May occur when the index does not have a freq
@@ -912,7 +916,10 @@ def predict(self, params, start=None, end=None):
         """
         if start is None:
             freq = getattr(self._index, 'freq', 1)
-            start = self._index[-1] + freq
+            if isinstance(freq, int):
+                start = self._index.shape[0] + freq
+            else:
+                start = self._index[-1] + freq
         start, end, out_of_sample, prediction_index = self._get_prediction_index(
             start=start, end=end)
         if out_of_sample > 0:
@@ -1090,6 +1097,8 @@ def fit(self, smoothing_level=None, smoothing_slope=None, smoothing_seasonal=Non
                     # solution to parameters
                     _bounds = [bnd for bnd, flag in zip(bounds, xi) if flag]
                     lb, ub = np.asarray(_bounds).T.astype(float)
+                    ub[np.isnan(ub)] = np.inf
+                    lb[np.isnan(lb)] = -np.inf
                     initial_p = p[xi]
 
                     # Ensure strictly inbounds

diff --git a/statsmodels/tsa/tests/test_holtwinters.py b/statsmodels/tsa/tests/test_holtwinters.py
@@ -879,3 +879,14 @@ def test_simulate_boxcox(austourists):
     mean = np.mean(res, axis=1)
 
     assert np.all(np.abs(mean - expected) < 5)
+
+
+@pytest.mark.parametrize("ix", [10, 100, 1000, 2000])
+def test_forecast_index(ix):
+    # GH 6549
+    ts_1 = pd.Series([85601, 89662, 85122, 84400, 78250, 84434, 71072, 70357, 72635, 73210],
+                     index=range(ix, ix + 10))
+    model = ExponentialSmoothing(ts_1, trend='add', damped=False).fit()
+    index = model.forecast(steps=10).index
+    assert index[0] == ix + 10
+    assert index[-1] == ix + 19