class_docstr: Piecewise Aggregate Approximation Transformer (PAA).
  
  (PAA) Piecewise Aggregate Approximation Transformer, as described in
  Eamonn Keogh, Kaushik Chakrabarti, Michael Pazzani, and Sharad Mehrotra.
  Dimensionality reduction for fast similarity search in large time series
  databases.
  Knowledge and information Systems, 3(3), 263-286, 2001.
  For each series reduce the dimensionality to num_intervals, where each
  value is the mean of values in
  the interval.
  
  TO DO: pythonise it to make it more efficient. Maybe check vs this version
          http://vigne.sh/posts/piecewise-aggregate-approx/
  Could have: Tune the interval size in fit somehow?
  
  Parameters
  ----------
  num_intervals   : int, dimension of the transformed data (default 8)
code: def __init__(self, num_intervals=8):
      self.num_intervals = num_intervals
      super().__init__()
  
file_path: sktime/sktime/transformations/panel/dictionary_based/_paa.py
imports: import pandas
  from sktime.datatypes._panel._convert import from_nested_to_2d_array
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 1 to create a new PAA object from inherited base classes: BaseTransformer with example: obj = PAA(num_intervals)