Fix params

statsmodels/tsa/holtwinters/model.py

@@ -13,6 +13,7 @@
 """
 from statsmodels.compat.pandas import deprecate_kwarg
 
+import contextlib
 from typing import Any, Hashable, Sequence
 import warnings
 
@@ -166,7 +167,7 @@ class ExponentialSmoothing(TimeSeriesModel):
         excluding the initial values if estimated. The parameters are ordered
         [alpha, beta, gamma, phi].  Each element is a tuple of the form
         (lower, upper). Default is (0.0001, 0.9999) for the level, trend, and
-        seasonal smoothing parameters and (0.8, 0.98) for the trend damping
+        seasonal smoothing parameters and (0.8, 0.995) for the trend damping
         parameter.
 
     Returns
@@ -265,10 +266,34 @@ def __init__(
         self._estimate_level = estimated
         self._estimate_trend = estimated and self.trend
         self._estimate_seasonal = estimated and self.seasonal
+        self._bounds = self._check_bounds(bounds)
         self._use_boxcox = use_boxcox
         self._lambda = np.nan
         self._y = self._boxcox()
         self._initialize()
+        self._fixed_parameters = {}
+
+    def _check_bounds(self, bounds):
+        if bounds is None:
+            return
+        msg = (
+            "bounds must be a list of 2-element tuples of the form"
+            " (lb, ub) where lb < ub, lb>=0 and ub<=1"
+        )
+        try:
+            bounds_len = len(bounds) != 4
+        except:
+            raise TypeError(msg)
+        if bounds_len != 4:
+            raise TypeError(msg)
+        for bound in bounds:
+            if not isinstance(bound, tuple):
+                raise TypeError(msg)
+            if len(bound) != 2 or bound[0] >= bound[1]:
+                raise ValueError(msg)
+            if bound[0] < 0.0 or bound[1] > 1.0:
+                raise ValueError(msg)
+        return list(bounds)
 
     def _boxcox(self):
         if (
@@ -291,6 +316,53 @@ def _boxcox(self):
             raise TypeError("use_boxcox must be True, False or a float.")
         return y
 
+    @contextlib.contextmanager
+    def fix_params(self, values):
+        """
+        Temporarily fix parameters for estimation
+
+        Parameters
+        ----------
+        values : dict
+            Values to fix. The key is the parameter name and the value is the
+            fixed value.
+
+        Examples
+        --------
+        # TODO
+        """
+        values = dict_like(values, "values")
+        valid_keys = ("smoothing_level",)
+        if self.has_trend:
+            valid_keys += ("smoothing_trend",)
+        if self.has_seasonal:
+            valid_keys += ("smoothing_seasonal",)
+            valid_keys += tuple(
+                [f"initial_seasonal.{i}" for i in range(self.seasonal_periods)]
+            )
+        if self.damped_trend:
+            valid_keys += ("damping_trend",)
+        if self._initialization_method in ("estimated", None):
+            extra_keys = [
+                key.replace("smoothing_", "initial_")
+                for key in valid_keys
+                if "smoothing_" in key
+            ]
+            valid_keys += tuple(extra_keys)
+
+        for key in values:
+            if key not in valid_keys:
+                valid = ", ".join(valid_keys[:-1]) + ", and " + valid_keys[-1]
+                raise KeyError(
+                    f"{key} if not allowed. Only {valid} are supported in this specification."
+                )
+
+        try:
+            self._fixed_parameters = values
+            yield
+        finally:
+            self._fixed_parameters = {}
+
     def _initialize(self):
         if self._initialization_method is None:
             warnings.warn(
@@ -487,6 +559,9 @@ def _optimize_parameters(
             (0.8, 0.995),  # phi
         ]
         bounds += [(None, None)] * m
+        if self._bounds is not None:
+            for i, loc in enumerate((0, 1, 2, 5)):
+                bounds[loc] = self._bounds[i]
         hw_args = HoltWintersArgs(sel.astype(int), params, y, m, self.nobs)
 
         if start_params is None and use_brute and np.any(sel[:3]):