SIENTIAPDE-1430: Introduce static_threshold parameter for static window removal.

This parameter allows customizing the threshold (1-1000) used when rem_static_win is enabled, defaulting to 1 if null.
Updates include parameter definition, business rule validation, repository logic for passing the threshold, documentation in README.md and PIPELINE_PARAMS_CHANGELOG.md, and new unit and integration tests.

model_manager/utils/models/train_model_params.py

@@ -47,6 +47,7 @@ class TrainModelParams:
         end_date (str | None): End date for filtering data.
         scaler_name (str): Name of the scaler to use ('Standard Scaler' or 'None').
         support_filters (dict): Custom support filters per variable.
+        static_threshold (int | None): Threshold for static window removal (1-1000). Only used when rem_static_win is True.
     """
 
     variable_columns: list[str]
@@ -76,6 +77,7 @@ class TrainModelParams:
     end_date: str | None
     scaler_name: str
     support_filters: dict
+    static_threshold: int | None
 
     @classmethod
     def from_dict(cls, data: dict[str, Any]) -> 'TrainModelParams':
@@ -138,6 +140,7 @@ class TrainModelParams:
             scaler_name=cls._check_none(data.get('scaler_name'), str, 'scaler_name'),
             support_filters=cls._check_type(data.get('support_filters'), dict, 'support_filters')
             or {},
+            static_threshold=cls._check_type(data.get('static_threshold'), int, 'static_threshold'),
         )
 
     @staticmethod
@@ -228,6 +231,13 @@ class TrainModelParams:
         if self.window < 0:
             raise ValueError(f'window must be non-negative, got {self.window}')
 
+        # Validate static_threshold only when rem_static_win is True and value is provided
+        if self.rem_static_win and self.static_threshold is not None:
+            if not 1 <= self.static_threshold <= 1000:
+                raise ValueError(
+                    f'static_threshold must be between 1 and 1000, got {self.static_threshold}'
+                )
+
     def _validate_model_params(self) -> None:
         """Validate model-related parameters."""
         if self.degree < 1:

model_manager/utils/repository/model_repository.py

@@ -206,7 +206,10 @@ class ModelRepository:
             self.model_serving.log_param('lag_train', data.params.lag_train)
             self.model_serving.log_param('lag_transform', data.params.lag_val)
             self.model_serving.log_param(
-                'static_threshold', 1 if data.params.rem_static_win else None
+                'static_threshold',
+                (data.params.static_threshold if data.params.static_threshold is not None else 1)
+                if data.params.rem_static_win
+                else None,
             )
             self.model_serving.log_param('lower_limits', data.params.low_lim)
             self.model_serving.log_param('upper_limits', data.params.upp_lim)

model_manager/utils/repository/training_repository.py

@@ -269,7 +269,9 @@ class TrainingRepository:
             start_date=params.start_date,
             end_date=params.end_date,
             removed_intervals=removed_intervals,
-            static_threshold=1 if params.rem_static_win else None,
+            static_threshold=(params.static_threshold if params.static_threshold is not None else 1)
+            if params.rem_static_win
+            else None,
             low_lim=params.low_lim,
             upp_lim=params.upp_lim,
             scaler_name=params.scaler_name,