MAINT: Improve style

Author: bashtage

statsmodels/tsa/forecasting/tests/test_theta.py

@@ -136,10 +136,26 @@ def test_forecast_seasonal_alignment(data, period):
 
 
 def test_auto(reset_randomstate):
-    e = np.random.standard_normal(100).cumsum()
-    y = 10 + e - e.min()
+    m = 250
+    e = np.random.standard_normal(m)
+    s = 10 * np.sin(np.linspace(0, np.pi, 12))
+    s = np.tile(s, (m // 12 + 1))[:m]
+    idx = pd.period_range("2000-01-01", freq="M", periods=m)
+    x = e + s
+    y = pd.DataFrame(10 + x - x.min(), index=idx)
+
     tm = ThetaModel(y, method="auto")
     assert tm.method == "mul"
+    res = tm.fit()
+
+    tm = ThetaModel(y, method="mul")
+    assert tm.method == "mul"
+    res2 = tm.fit()
+
+    np.testing.assert_allclose(res.params, res2.params)
+
+    tm = ThetaModel(y - y.mean(), method="auto")
+    assert tm.method == "add"
+    res3 = tm.fit()
 
-    tm = ThetaModel(y - 20, method="auto")
-    assert tm.method == "add"
+    assert not np.allclose(res.params, res3.params)

statsmodels/tsa/forecasting/theta.py

@@ -151,6 +151,8 @@ def __init__(
             "model",
             options=("auto", "additive", "multiplicative", "mul", "add"),
         )
+        if self._method == "auto":
+            self._method = "mul" if self._y.min() > 0 else "add"
         if self._period is None and self._deseasonalize:
             idx = getattr(endog, "index", None)
             pfreq = None
@@ -183,8 +185,6 @@ def _deseasonalize_data(self) -> Tuple[np.ndarray, np.ndarray]:
         y = self._y
         if not self._has_seasonality:
             return self._y, np.empty(0)
-        if self._method == "auto":
-            self._method = "mul" if self._y.min() > 0 else "add"
 
         res = seasonal_decompose(y, model=self._method, period=self._period)
         if res.seasonal.min() <= 0: