class_docstr: Light gradient boosting (LGBM) regressor for time series forecasting
  
  LightGBM is a light weight and fast gradient boosting framework that uses tree based learning algorithms, for more
  details, please refer to the document https://lightgbm.readthedocs.io/en/latest/Features.html
class_docstr_tok: ["Light","gradient","boosting","(","LGBM",")","regressor","for","time","series","forecasting","LightGBM","is","a","light","weight","and","fast","gradient","boosting","framework","that","uses","tree","based","learning","algorithms",",","for","more","details",",","please","refer","to","the","document","https",":","\/\/lightgbm.readthedocs.io\/en\/latest\/Features.html"]
code: def __init__(self, config: LGBMForecasterConfig):
      super().__init__(config)
      self.model = MultiOutputRegressor(
          LGBMRegressor(
              learning_rate=self.config.learning_rate,
              n_estimators=self.config.n_estimators,
              max_depth=self.config.max_depth,
              random_state=self.config.random_state,
              n_jobs=self.config.n_jobs,
          ),
          n_jobs=1,
      )
  
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 LGBMForecaster object from inherited base classes: SKLearnForecaster with example: obj = LGBMForecaster(config)