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: Convert a time series into a point cloud in the dimension specified by dimension_embed.
  
  Args:
      input_data: Time series to be converted.
      dimension_embed: dimension of Euclidean space in which to embed the time series into by taking
      windows of dimension_embed length, e.g. if the time series is ``[t_1,...,t_n]`` and dimension_embed
      is ``2``, then the point cloud would be ``[(t_0, t_1), (t_1, t_2),...,(t_(n-1), t_n)]``
  
  Returns:
      Collection of points embedded into Euclidean space of dimension = dimension_embed, constructed
      in the manner explained above.
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code: def time_series_to_point_cloud(
      self, input_data: np.array = None, dimension_embed=2
  ) -> np.array:
      """Convert a time series into a point cloud in the dimension specified by dimension_embed.
  
      Args:
          input_data: Time series to be converted.
          dimension_embed: dimension of Euclidean space in which to embed the time series into by taking
          windows of dimension_embed length, e.g. if the time series is ``[t_1,...,t_n]`` and dimension_embed
          is ``2``, then the point cloud would be ``[(t_0, t_1), (t_1, t_2),...,(t_(n-1), t_n)]``
  
      Returns:
          Collection of points embedded into Euclidean space of dimension = dimension_embed, constructed
          in the manner explained above.
  
      """
  
      if self.__window_length is None:
          self.__window_length = dimension_embed
  
      trajectory_transformer = HankelMatrix(
          time_series=input_data,
          window_size=self.__window_length,
          strides=self.stride,
      )
      return trajectory_transformer.trajectory_matrix
  
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 3 for calling the TopologicalTransformation obj's underlying member method code with example usage: obj.time_series_to_point_cloud(input_data, dimension_embed) and returns: trajectory_transformer