class_docstr: Transforms a time series into a differentiated time series of order n.
  It also reverts the differentiation.
  
  Parameters
  ----------
  order : int
      Order of differentiation.
  
  Attributes
  ----------
  order : int
      Order of differentiation.
  initial_values : list
      List with the initial value of the time series after each differentiation.
      This is used to revert the differentiation.
  last_values : list
      List with the last value of the time series after each differentiation.
      This is used to revert the differentiation of a new window of data. A new
      window of data is a time series that starts right after the time series
      used to fit the transformer.
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code: def __init__(self, order: int = 1) -> None:
      if not isinstance(order, int):
          raise TypeError(
              f"Parameter 'order' must be an integer. Found {type(order)}."
          )
      if order < 1:
          raise ValueError(
              f"Parameter 'order' must be an integer greater than 0. Found {order}."
          )
  
      self.order = order
      self.initial_values = []
      self.last_values = []
  
file_path: skforecast/skforecast/preprocessing/preprocessing.py
imports: import numpy
  from sklearn.base import BaseEstimator
  from sklearn.base import TransformerMixin
  from typing import Any
  from typing_extensions import Self
label_desc: Use image node_id 1 to create a new TimeSeriesDifferentiator object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = TimeSeriesDifferentiator(order)