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)
docstr: Check parameters of PAA.
  
  Function for checking the values of parameters inserted into PAA.
  For example, the number of subsequences cannot be larger than the
  time series length.
  
  Throws
  ------
  ValueError or TypeError if a parameters input is invalid.
code: def _check_parameters(self, num_atts):
      """Check parameters of PAA.
  
      Function for checking the values of parameters inserted into PAA.
      For example, the number of subsequences cannot be larger than the
      time series length.
  
      Throws
      ------
      ValueError or TypeError if a parameters input is invalid.
      """
      if isinstance(self.num_intervals, int):
          if self.num_intervals <= 0:
              raise ValueError(
                  "num_intervals must have the \
                                    value of at least 1"
              )
          if self.num_intervals > num_atts:
              raise ValueError(
                  "num_intervals cannot be higher \
                                    than the time series length."
              )
      else:
          raise TypeError(
              "num_intervals must be an 'int'. Found '"
              + type(self.num_intervals).__name__
              + "' instead."
          )
  
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 5 for calling the PAA obj's underlying member method code with example usage: obj._check_parameters(num_atts) without return types