class_docstr: StatsForecast ARCH estimator.
  
  Direct interface to ``statsforecast.models.ARCH``.
  
  This implements the Autoregressive Conditional
  Heteroskedasticity (ARCH) model.
  
  Constructs an ARCH(p) model.
  
  Parameters
  ----------
  p: int (default 1)
      AR parameter - number of auto-regressive lags.
class_docstr_tok: ["StatsForecast","ARCH","estimator",".","Direct","interface","to","``","statsforecast.models.ARCH","``",".","This","implements","the","Autoregressive","Conditional","Heteroskedasticity","(","ARCH",")","model",".","Constructs","an","ARCH","(","p",")","model",".","Parameters","--","--","--","--","--","p",":","int","(","default","1",")","AR","parameter","-","number","of","auto-regressive","lags","."]
code: def __init__(
      self,
      p=1,
  ):
      self.p = p
  
      super().__init__()
  
file_path: sktime/sktime/forecasting/arch/_statsforecast_arch.py
imports: from sktime.forecasting.base.adapters._generalised_statsforecast import _GeneralisedStatsForecastAdapter
label_desc: Use image node_id 1 to create a new StatsForecastARCH object from inherited base classes: _GeneralisedStatsForecastAdapter with example: obj = StatsForecastARCH(p)