docstr: The lagged feature names the model has been trained on.
  
  The naming convention for target, past and future covariates is: ``"{name}_{type}_lag{i}"``, where:
  
      - ``{name}`` the component name of the (first) series
      - ``{type}`` is the feature type, one of "target", "pastcov", and "futcov"
      - ``{i}`` is the lag value
  
  The naming convention for static covariates is: ``"{name}_statcov_target_{comp}"``, where:
  
      - ``{name}`` the static covariate name of the (first) series
      - ``{comp}`` the target component name of the (first) that the static covariate act on. If the static
          covariate acts globally on a multivariate target series, will show "global".
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code: def lagged_feature_names(self) -> Optional[List[str]]:
      """The lagged feature names the model has been trained on.
  
      The naming convention for target, past and future covariates is: ``"{name}_{type}_lag{i}"``, where:
  
          - ``{name}`` the component name of the (first) series
          - ``{type}`` is the feature type, one of "target", "pastcov", and "futcov"
          - ``{i}`` is the lag value
  
      The naming convention for static covariates is: ``"{name}_statcov_target_{comp}"``, where:
  
          - ``{name}`` the static covariate name of the (first) series
          - ``{comp}`` the target component name of the (first) that the static covariate act on. If the static
              covariate acts globally on a multivariate target series, will show "global".
      """
      return self._lagged_feature_names
  
imports: from collections import OrderedDict
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import pandas
  from sklearn.linear_model import LinearRegression
  from darts.logging import get_logger, raise_if, raise_if_not, raise_log
  from darts.models.forecasting.forecasting_model import GlobalForecastingModel
  from darts.timeseries import TimeSeries
  from darts.utils.data.tabularization import add_static_covariates_to_lagged_data, create_lagged_component_names, create_lagged_training_data
  from darts.utils.historical_forecasts import _check_optimizable_historical_forecasts_global_models, _optimized_historical_forecasts_all_points, _optimized_historical_forecasts_last_points_only, _process_historical_forecast_input
  from darts.utils.multioutput import MultiOutputRegressor
  from darts.utils.utils import _check_quantiles, get_single_series, seq2series, series2seq
label_desc: Use image node_id 16 for calling the RegressionModel obj's underlying member method code with example usage: obj.lagged_feature_names() and returns: self