Repository: statsmodels/statsmodels (Python)

Issue:
Theta deseasonalizing multiplicative method not working
#### Describe the bug

When using the forecasting Theta model I found some strange behavior in the deseasonalizing of the data. There should be three deseasonalize methods: “auto”, “additive” and “multiplicative” (https://www.statsmodels.org/dev/generated/statsmodels.tsa.forecasting.theta.ThetaModel.html#statsmodels.tsa.forecasting.theta.ThetaModel). However, when I use the "auto" method, the output is not equal to the ouput of the “additive” or “multiplicative” method.

I had a look in the source code and suspect that this is caused by the  _deseasonalize_data() function in the Theta model. In this function the following code is used:

        **self._method = (
            "mul" if self._method == "auto" and self._y.min() > 0 else "add"
        )**

The "multiplicative” method is not considered here and is returned as "additive" if I'm not mistaking. This should not be the case if you can give it as input.


<details>

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</details>


#### Expected Output

Solve the issue. When for deseasonalizing the "multiplicative" method is selected, it should also be used.

#### Output of ``import statsmodels.api as sm; sm.show_versions()``

<details>

[paste the output of ``import statsmodels.api as sm; sm.show_versions()`` here below this line]

INSTALLED VERSIONS
------------------
Python: 3.9.12.final.0
statsmodels
===========
Installed: 0.13.0 (C:\Users\CasRoelants\anaconda3\envs\honeycomb-env\lib\site-packages\statsmodels)
Required Dependencies
=====================
cython: 0.29.24 (C:\Users\CasRoelants\anaconda3\envs\honeycomb-env\lib\site-packages\Cython)
numpy: 1.21.4 (C:\Users\CasRoelants\anaconda3\envs\honeycomb-env\lib\site-packages\numpy)
scipy: 1.5.4 (C:\Users\CasRoelants\anaconda3\envs\honeycomb-env\lib\site-packages\scipy)
pandas: 1.3.4 (C:\Users\CasRoelants\anaconda3\envs\honeycomb-env\lib\site-packages\pandas)
    dateutil: 2.8.2 (C:\Users\CasRoelants\anaconda3\envs\honeycomb-env\lib\site-packages\dateutil)
patsy: 0.5.2 (C:\Users\CasRoelants\anaconda3\envs\honeycomb-env\lib\site-packages\patsy)
Optional Dependencies
=====================
matplotlib: 3.5.2 (C:\Users\CasRoelants\anaconda3\envs\honeycomb-env\lib\site-packages\matplotlib)
Backend TkAgg is interactive backend. Turning interactive mode on.
    backend: TkAgg 
cvxopt: Not installed
joblib: 1.1.0 (C:\Users\CasRoelants\anaconda3\envs\honeycomb-env\lib\site-packages\joblib)
Developer Tools
================
IPython: 8.4.0 (C:\Users\CasRoelants\anaconda3\envs\honeycomb-env\lib\site-packages\IPython)
    jinja2: 3.1.2 (C:\Users\CasRoelants\anaconda3\envs\honeycomb-env\lib\site-packages\jinja2)
sphinx: 4.5.0 (C:\Users\CasRoelants\anaconda3\envs\honeycomb-env\lib\site-packages\sphinx)
    pygments: 2.12.0 (C:\Users\CasRoelants\anaconda3\envs\honeycomb-env\lib\site-packages\pygments)
pytest: 6.2.5 (C:\Users\CasRoelants\anaconda3\envs\honeycomb-env\lib\site-packages\pytest)
virtualenv: 20.16.2 (C:\Users\CasRoelants\anaconda3\envs\honeycomb-env\lib\site-packages\virtualenv)



Hints:
Any updates?

Patch:
diff --git a/statsmodels/tsa/forecasting/theta.py b/statsmodels/tsa/forecasting/theta.py
--- a/statsmodels/tsa/forecasting/theta.py
+++ b/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
@@ -183,9 +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)
-        self._method = (
-            "mul" if self._method == "auto" and self._y.min() > 0 else "add"
-        )
 
         res = seasonal_decompose(y, model=self._method, period=self._period)
         if res.seasonal.min() <= 0:


Test patch:
diff --git a/statsmodels/tsa/forecasting/tests/test_theta.py b/statsmodels/tsa/forecasting/tests/test_theta.py
--- a/statsmodels/tsa/forecasting/tests/test_theta.py
+++ b/statsmodels/tsa/forecasting/tests/test_theta.py
@@ -133,3 +133,29 @@ def test_forecast_seasonal_alignment(data, period):
     index = np.arange(data.shape[0], data.shape[0] + comp.shape[0])
     expected = seasonal[index % period]
     np.testing.assert_allclose(comp.seasonal, expected)
+
+
+def test_auto(reset_randomstate):
+    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()
+
+    assert not np.allclose(res.params, res3.params)