ForecastingWorkflowConfig

class openstef_models.presets.forecasting_workflow.ForecastingWorkflowConfig(**data: Any) → None

Bases: BaseConfig

Configuration for forecasting workflows.

Defines all parameters needed to set up a forecasting model, including model type, hyperparameters, location information, data columns, and feature engineering settings.

Parameters:

data (Any)

kind: Literal['single']

model_id: TypeAliasType

run_name: str | None

model: Literal['xgboost', 'gblinear', 'flatliner', 'median', 'constant_quantile', 'lgbm', 'lgbmlinear']

quantiles: list[Quantile]

sample_interval: timedelta

horizons: list[LeadTime]

xgboost_hyperparams: XGBoostHyperParams

gblinear_hyperparams: GBLinearHyperParams

lgbm_hyperparams: LGBMHyperParams

lgbmlinear_hyperparams: LGBMLinearHyperParams

location: LocationConfig

target_column: str

energy_price_column: str

radiation_column: str

wind_speed_column: str

pressure_column: str

temperature_column: str

relative_humidity_column: str

selected_features: FeatureSelection

predict_history: timedelta

cutoff_history: timedelta

completeness_threshold: float

completeness_threshold_target_constant_quantile: float

flatliner_threshold: timedelta

detect_non_zero_flatliner: bool

predict_nonzero_flatliner: bool

shifters: list[Shifter]

rolling_aggregate_features: list[TypeAliasType]

clip_features: FeatureSelection

nan_on_outlier_features: FeatureSelection

max_day_lags: int

sample_weight_config: SampleWeightConfig

data_splitter: DataSplitter

evaluation_metrics: list[MetricProvider]

mlflow_storage: MLFlowStorage | None

model_reuse_enable: bool

model_reuse_max_age: timedelta

model_selection_enable: bool

model_selection_metric: tuple[Union[Quantile, Literal['global']], str, TypeAliasType]

model_selection_old_model_penalty: float

model_performance_callback_enabled: bool

model_performance_callback_metric_threshold: tuple[Union[Quantile, Literal['global']], str, TypeAliasType, float]

verbosity: Literal[0, 1, 2, 3, True]

tags: dict[str, str]

experiment_tags: dict[str, str]

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': False, 'extra': 'ignore', 'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].