class_docstr: This class defines the certain data operation implementation for time series
  forecasting
  
  Args:
      operation_type: str type of the operation defined in operation or
          data operation repositories
      params: hyperparameters to fit the model with
class_docstr_tok: ["This","class","defines","the","certain","data","operation","implementation","for","time","series","forecasting","Args",":","operation_type",":","str","type","of","the","operation","defined","in","operation","or","data","operation","repositories","params",":","hyperparameters","to","fit","the","model","with"]
docstr: This method is used for operation training with the data provided
  :param InputData train_data: data used for operation training
  :return: trained operation (if it is needed for applying)
docstr_tok: ["This","method","is","used","for","operation","training","with","the","data","provided",":","param","InputData","train_data",":","data","used","for","operation","training",":","return",":","trained","operation","(","if","it","is","needed","for","applying",")"]
code: def fit(self, train_data: InputData):
      """
      This method is used for operation training with the data provided
      :param InputData train_data: data used for operation training
      :return: trained operation (if it is needed for applying)
      """
      warnings.filterwarnings("ignore", category=RuntimeWarning)
  
      transformation_operation = self.operation(self.params_for_fit)
      with ImplementationRandomStateHandler(
          implementation=transformation_operation
      ):
          transformation_operation.fit(train_data)
      return transformation_operation
  
imports: import warnings
  from typing import Optional
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.operations.evaluation.evaluation_interfaces import EvaluationStrategy
  from fedot.core.operations.evaluation.operation_implementations.data_operations.ts_transformations import ExogDataTransformationImplementation, GaussianFilterImplementation, LaggedTransformationImplementation, TsSmoothingImplementation, SparseLaggedTransformationImplementation, CutImplementation, NumericalDerivativeFilterImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.arima import ARIMAImplementation, STLForecastARIMAImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.cgru import CGRUImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.naive import RepeatLastValueImplementation, NaiveAverageForecastImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.poly import PolyfitImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.ts_implementations.statsmodels import AutoRegImplementation, GLMImplementation, ExpSmoothingImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.utilities.random import ImplementationRandomStateHandler
label_desc: Use image node_id 2 for calling the FedotTsTransformingStrategy obj's underlying member method code with example usage: obj.fit(train_data) and returns: transformation_operation