docstr: Method for time series prediction on forecast length
  
  :param input_data: data with features, target and ids to process
  :return output_data: output data with smoothed time series
code: def predict(self, input_data):
      """Method for time series prediction on forecast length
  
      :param input_data: data with features, target and ids to process
      :return output_data: output data with smoothed time series
      """
      input_data = copy(input_data)
      parameters = input_data.task.task_params
      forecast_length = parameters.forecast_length
  
      # in case in(out) sample forecasting
      self.handle_new_data(input_data)
      start_id = self.actual_ts_len
      end_id = start_id + forecast_length - 1
      predicted = self.autoreg.predict(start=start_id, end=end_id)
      predict = np.array(predicted).reshape(1, -1)
  
      output_data = self._convert_to_output(
          input_data, predict=predict, data_type=DataTypesEnum.table
      )
      return output_data
  
imports: from copy import copy
  import numpy
  import statsmodels.api
  from statsmodels.genmod.families import Gamma, Gaussian, InverseGaussian
  from statsmodels.genmod.families.links import identity, inverse_power, inverse_squared, log
  from statsmodels.genmod.generalized_linear_model import GLM
  from statsmodels.tsa.ar_model import AutoReg
  from statsmodels.tsa.exponential_smoothing.ets import ETSModel
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.operations.evaluation.operation_implementations.data_operations.ts_transformations import ts_to_table
  from fedot.core.operations.evaluation.operation_implementations.implementation_interfaces import ModelImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.core.repository.dataset_types import DataTypesEnum
label_desc: Use image node_id 3 for calling the AutoRegImplementation obj's underlying member method code with example usage: obj.predict(input_data) and returns: output_data