class_docstr: Decomposes the given time series with a singular-spectrum analysis. Assumes the values of the time series are
  recorded at equal intervals.
  
  Args:
      time_series: Time series to be decomposed.
      max_simplex_dim: Maximum dimension of the simplices to be used in the Rips filtration.
      epsilon: Maximum distance between two points to be considered connected by an edge in the Rips filtration.
      persistence_params: ...
      window_length: Length of the window to be used in the rolling window function.
  
  Attributes:
      epsilon_range (np.ndarray): Range of epsilon values to be used in the Rips filtration.
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docstr: Wrapper function that takes in a time series and outputs the persistent homology object, along with other
  auxiliary objects.
  
  Args:
      time_series: Time series to be converted.
      max_simplex_dim: Maximum dimension of the simplicial complex to be constructed.
  
  Returns:
      Persistent homology object. Dictionary with keys in ``range(max_simplex_dim)`` and, the value ``hom[i]``
      is an array of length equal to ``len(epsilon_range)`` containing the betti numbers of the ``i-th`` homology
      groups for the Rips filtration.
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code: def time_series_to_persistent_cohomology_ripser(
      self, time_series: np.array, max_simplex_dim: int
  ) -> dict:
      """Wrapper function that takes in a time series and outputs the persistent homology object, along with other
      auxiliary objects.
  
      Args:
          time_series: Time series to be converted.
          max_simplex_dim: Maximum dimension of the simplicial complex to be constructed.
  
      Returns:
          Persistent homology object. Dictionary with keys in ``range(max_simplex_dim)`` and, the value ``hom[i]``
          is an array of length equal to ``len(epsilon_range)`` containing the betti numbers of the ``i-th`` homology
          groups for the Rips filtration.
  
      """
  
      homology = self.point_cloud_to_persistent_cohomology_ripser(
          point_cloud=time_series, max_simplex_dim=max_simplex_dim
      )
      return homology
  
file_path: FEDOT/fedot/core/operations/evaluation/operation_implementations/data_operations/topological/point_cloud.py
imports: import numpy
  import pandas
  from ripser import Rips
  from fedot.core.operations.evaluation.operation_implementations.data_operations.topological.hankel_matrix import HankelMatrix
label_desc: Use image node_id 5 for calling the TopologicalTransformation obj's underlying member method code with example usage: obj.time_series_to_persistent_cohomology_ripser(time_series, max_simplex_dim) and returns: homology