class_docstr: Class used for filling in the gaps in time series
  
  Args:
      gap_value: value, which mask gap elements in array
      pipeline: TsForecastingPipeline object for filling in the gaps
class_docstr_tok: ["Class","used","for","filling","in","the","gaps","in","time","series","Args",":","gap_value",":","value",",","which","mask","gap","elements","in","array","pipeline",":","TsForecastingPipeline","object","for","filling","in","the","gaps"]
docstr: The time series method makes a forward forecast based on the part
  of the time series that is located to the left of the gap.
  
  Args:
      output_data: one-dimensional array of a time series
      batch_index: index of the interval (batch) with a gap
      new_gap_list: array with nested lists of gap indexes
  
  Returns:
      weights_list: numpy array with prediction weights for averaging
      predicted: numpy array with prediction values in the gap
docstr_tok: ["The","time","series","method","makes","a","forward","forecast","based","on","the","part","of","the","time","series","that","is","located","to","the","left","of","the","gap",".","Args",":","output_data",":","one-dimensional","array","of","a","time","series","batch_index",":","index","of","the","interval","(","batch",")","with","a","gap","new_gap_list",":","array","with","nested","lists","of","gap","indexes","Returns",":","weights_list",":","numpy","array","with","prediction","weights","for","averaging","predicted",":","numpy","array","with","prediction","values","in","the","gap"]
code: def _forward(self, output_data, batch_index, new_gap_list):
      """The time series method makes a forward forecast based on the part
      of the time series that is located to the left of the gap.
  
      Args:
          output_data: one-dimensional array of a time series
          batch_index: index of the interval (batch) with a gap
          new_gap_list: array with nested lists of gap indexes
  
      Returns:
          weights_list: numpy array with prediction weights for averaging
          predicted: numpy array with prediction values in the gap
      """
  
      gap = new_gap_list[batch_index]
      first_gap_element_id = gap[0]
      timeseries_train_part = output_data[:first_gap_element_id]
  
      # Adaptive prediction interval length
      len_gap = len(gap)
      predicted = self.__forecast_in_gap(
          self.pipeline, timeseries_train_part, output_data, gap
      )
      weights_list = np.arange(len_gap, 0, -1)
      return weights_list, predicted
  
imports: from copy import deepcopy
  from typing import List, Union
  import numpy
  from golem.core.log import default_log
  from scipy import interpolate
  from fedot.core.data.data import InputData
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TaskTypesEnum, TsForecastingParams
label_desc: Use image node_id 4 for calling the ModelGapFiller obj's underlying member method code with example usage: obj._forward(output_data, batch_index, new_gap_list) and returns: weights_list,  predicted