docstr: This encoder handles arrays of categorical time series data by wrapping the OHE encoder with behavior specific to time series tasks.
  
  :param timesteps: length of forecasting horizon, as defined by TimeseriesSettings.window.
  :param is_target: whether this encoder corresponds to the target column.
  :param grouped_by: what columns, if any, are considered to group the original column and yield multiple time series.
docstr_tok: ["This","encoder","handles","arrays","of","categorical","time","series","data","by","wrapping","the","OHE","encoder","with","behavior","specific","to","time","series","tasks",".",":","param","timesteps",":","length","of","forecasting","horizon",",","as","defined","by","TimeseriesSettings.window",".",":","param","is_target",":","whether","this","encoder","corresponds","to","the","target","column",".",":","param","grouped_by",":","what","columns",",","if","any",",","are","considered","to","group","the","original","column","and","yield","multiple","time","series","."]
code: def __init__(
      self, timesteps: int, is_target: bool = False, grouped_by=None
  ):
      """
      This encoder handles arrays of categorical time series data by wrapping the OHE encoder with behavior specific to time series tasks.
  
      :param timesteps: length of forecasting horizon, as defined by TimeseriesSettings.window.
      :param is_target: whether this encoder corresponds to the target column.
      :param grouped_by: what columns, if any, are considered to group the original column and yield multiple time series.
      """  # noqa
      super(TsCatArrayEncoder, self).__init__(is_target=is_target)
      self.group_combinations = None
      self.dependencies = grouped_by
      self.data_window = timesteps
      self.sub_encoder = OneHotEncoder(
          is_target=is_target, use_unknown=False
      )
  
file_path: lightwood/lightwood/encoder/array/ts_cat_array.py
imports: from typing import List, Dict, Iterable, Optional
  import torch
  import torch.nn.functional
  from lightwood.encoder import BaseEncoder
  from lightwood.encoder.categorical import OneHotEncoder
label_desc: Use image node_id 1 to create a new TsCatArrayEncoder object from inherited base classes: BaseEncoder with example: obj = TsCatArrayEncoder(timesteps, is_target, grouped_by)