class_docstr: Base class for interfacing tbats forecasting algorithms.
class_docstr_tok: ["Base","class","for","interfacing","tbats","forecasting","algorithms","."]
code: def __init__(
      self,
      use_box_cox=None,
      box_cox_bounds=(0, 1),
      use_trend=None,
      use_damped_trend=None,
      sp=None,
      use_arma_errors=True,
      show_warnings=True,
      n_jobs=None,
      multiprocessing_start_method="spawn",
      context=None,
  ):
      self.use_box_cox = use_box_cox
      self.box_cox_bounds = box_cox_bounds
      self.use_trend = use_trend
      self.use_damped_trend = use_damped_trend
      self.sp = sp
      self.use_arma_errors = use_arma_errors
      self.show_warnings = show_warnings
      self.n_jobs = n_jobs
      self.multiprocessing_start_method = multiprocessing_start_method
      self.context = context
      # custom sktime args
      self._forecaster = None
      self._yname = None  # .fit(y) -> y.name
  
      super().__init__()
  
file_path: sktime/sktime/forecasting/base/adapters/_tbats.py
imports: import numpy
  import pandas
  from sktime.forecasting.base import BaseForecaster
  from sktime.utils.validation import check_n_jobs
  from sktime.utils.validation.forecasting import check_sp
label_desc: Use image node_id 1 to create a new _TbatsAdapter object from inherited base classes: BaseForecaster with example: obj = _TbatsAdapter(use_box_cox, box_cox_bounds, use_trend, use_damped_trend, sp, use_arma_errors, show_warnings, n_jobs, multiprocessing_start_method, context)