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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docstr: Transforms a time series into a differentiated time series of order n and
  stores the values needed to revert the differentiation.
  
  Parameters
  ----------
  X : numpy ndarray
      Time series to be differentiated.
  y : Ignored
      Not used, present here for API consistency by convention.
  
  Returns
  -------
  X_diff : numpy ndarray
      Differentiated time series. The length of the array is the same as
      the original time series but the first n=`order` values are nan.
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code: def transform(self, X: np.ndarray, y: Any = None) -> np.ndarray:
      """
      Transforms a time series into a differentiated time series of order n and
      stores the values needed to revert the differentiation.
  
      Parameters
      ----------
      X : numpy ndarray
          Time series to be differentiated.
      y : Ignored
          Not used, present here for API consistency by convention.
  
      Returns
      -------
      X_diff : numpy ndarray
          Differentiated time series. The length of the array is the same as
          the original time series but the first n=`order` values are nan.
  
      """
  
      X_diff = np.diff(X, n=self.order)
  
      X_diff = np.append(
          (np.full(shape=self.order, fill_value=np.nan)), X_diff
      )
  
      return X_diff
  
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 3 for calling the TimeSeriesDifferentiator obj's underlying member method code with example usage: obj.transform(X, y) and returns: X_diff