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.
class_docstr_tok: ["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","."]
code: def time_series_rolling_betti_ripser(self, ts):
      point_cloud = self.rolling_window(
          array=ts, window=self.__window_length
      )
      homology = self.time_series_to_persistent_cohomology_ripser(
          point_cloud, max_simplex_dim=self.max_simplex_dim
      )
      df_features = pd.DataFrame(data=homology)
      cols = ["Betti_{}".format(i) for i in range(df_features.shape[1])]
      df_features.columns = cols
      df_features["Betti_sum"] = df_features.sum(axis=1)
      return df_features
  
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 6 for calling the TopologicalTransformation obj's underlying member method code with example usage: obj.time_series_rolling_betti_ripser(ts) and returns: df_features