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
docstr_tok: ["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: InputData):
      """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)
      forecast_length = input_data.task.task_params.forecast_length
      self.handle_new_data(input_data)
  
      start_id = self.actual_ts_len
      end_id = start_id + forecast_length - 1
  
      predicted = self.arima.predict(start=start_id, end=end_id)
      predicted = self._inverse_boxcox(
          predicted=predicted, lambda_param=self.lambda_value
      )
      predict = self._inverse_shift(predicted)
  
      predict = np.array(predict).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
  from typing import Optional
  import numpy
  from scipy import stats
  from scipy.special import boxcox, inv_boxcox
  from statsmodels.tsa.api import STLForecast
  from statsmodels.tsa.arima.model import ARIMA
  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
  from fedot.utilities.ts_gapfilling import SimpleGapFiller
label_desc: Use image node_id 3 for calling the ARIMAImplementation obj's underlying member method code with example usage: obj.predict(input_data) and returns: output_data