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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code: def rolling_window(self, array, window):
      if window <= 0:
          raise ValueError("Window size must be a positive integer.")
      if window > len(array):
          raise ValueError(
              "Window size cannot exceed the length of the array."
          )
      return np.array(
          [
              array[i : i + window]
              for i in range(len(array) - window + 1)
          ]
      )
  
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 7 for calling the TopologicalTransformation obj's underlying member method code with example usage: obj.rolling_window(array, window) and returns: np