docstr: Time series encoder. This module will pass the normalized series values, along with moving averages taken from the series' last `window` values.
  :param stop_after: time budget in seconds.
  :param window: expected length of array data.
  :param original_type: element-wise data type
code: def __init__(
      self,
      stop_after: float,
      window: int = None,
      is_target: bool = False,
      original_type: dtype = None,
  ):
      """
      Time series encoder. This module will pass the normalized series values, along with moving averages taken from the series' last `window` values.
      :param stop_after: time budget in seconds.
      :param window: expected length of array data.
      :param original_type: element-wise data type
      """  # noqa
      super().__init__(stop_after, window, is_target, original_type)
      self.max_mavg_offset = self.output_size
      self.output_size += self.max_mavg_offset
  
file_path: lightwood/lightwood/encoder/time_series/ts.py
imports: from typing import List, Iterable
  import torch
  from type_infer.dtype import dtype
  from lightwood.encoder.array import ArrayEncoder
label_desc: Use image node_id 1 to create a new TimeSeriesEncoder object from inherited base classes: ArrayEncoder with example: obj = TimeSeriesEncoder(stop_after, window, is_target, original_type)