class_docstr: Random Forest Regressor for time series forecasting
  
  Random Forest is a meta estimator that fits a number of classifying decision
  trees on various sub-samples of the dataset, and uses averaging to improve
  the predictive accuracy and control over-fitting.
class_docstr_tok: ["Random","Forest","Regressor","for","time","series","forecasting","Random","Forest","is","a","meta","estimator","that","fits","a","number","of","classifying","decision","trees","on","various","sub-samples","of","the","dataset",",","and","uses","averaging","to","improve","the","predictive","accuracy","and","control","over-fitting","."]
code: def __init__(self, config: RandomForestForecasterConfig):
      super().__init__(config)
      self.model = RandomForestRegressor(
          n_estimators=self.config.n_estimators,
          max_depth=self.config.max_depth,
          min_samples_split=self.config.min_samples_split,
          random_state=self.config.random_state,
      )
  
imports: import logging
  from lightgbm import LGBMRegressor
  from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor
  from sklearn.multioutput import MultiOutputRegressor
  from merlion.models.forecast.sklearn_base import SKLearnForecaster, SKLearnForecasterConfig
label_desc: Use image node_id 1 to create a new RandomForestForecaster object from inherited base classes: SKLearnForecaster with example: obj = RandomForestForecaster(config)