class_docstr: Class for storing conformal intervals metadata information.
class_docstr_tok: ["Class","for","storing","conformal","intervals","metadata","information","."]
code: def __init__(
      self,
      n_windows: int = 2,
      h: int = 1,
      method: str = "conformal_distribution",
  ):
      if n_windows < 2:
          raise ValueError(
              "You need at least two windows to compute conformal intervals"
          )
      allowed_methods = ["conformal_distribution"]
      if method not in allowed_methods:
          raise ValueError(f"method must be one of {allowed_methods}")
      self.n_windows = n_windows
      self.h = h
      self.method = method
  
file_path: statsforecast/statsforecast/utils.py
imports: from typing import Dict
  import math
  import os
  import warnings
  import numpy
  import pandas
  from scipy.stats import norm
  from utilsforecast.compat import DataFrame
  from utilsforecast.data import generate_series
label_desc: Use image node_id 1 to create a new ConformalIntervals object  with example: obj = ConformalIntervals(n_windows, h, method)