class_docstr: Example:
      >>> #The dataset is split into data, validation_data
      >>> model = ARIMAForecaster(p=2, q=2, seasonality_mode=False)
      >>> model.fit(data, validation_data)
      >>> predict_result = model.predict(horizon=24)
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docstr: Build a ARIMA Forecast Model.
  User can customize p, q, seasonality_mode, P, Q, m, metric for the ARIMA model,
  the differencing term (d) and seasonal differencing term (D) are automatically
  estimated from the data. For details of the ARIMA model hyperparameters, refer to
  https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.ARIMA.html#pmdarima.arima.ARIMA.
  
  :param p: hyperparameter p for the ARIMA model.
  :param q: hyperparameter q for the ARIMA model.
  :param seasonality_mode: hyperparameter seasonality_mode for the ARIMA model.
  :param P: hyperparameter P for the ARIMA model.
  :param Q: hyperparameter Q for the ARIMA model.
  :param m: hyperparameter m for the ARIMA model.
  :param metric: the metric for validation and evaluation. For regression, we support
      Mean Squared Error: ("mean_squared_error", "MSE" or "mse"),
      Mean Absolute Error: ("mean_absolute_error","MAE" or "mae"),
      Mean Absolute Percentage Error: ("mean_absolute_percentage_error", "MAPE", "mape")
      Cosine Proximity: ("cosine_proximity", "cosine")
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code: def __init__(
      self,
      p=2,
      q=2,
      seasonality_mode=True,
      P=3,
      Q=1,
      m=7,
      metric="mse",
  ):
      """
      Build a ARIMA Forecast Model.
      User can customize p, q, seasonality_mode, P, Q, m, metric for the ARIMA model,
      the differencing term (d) and seasonal differencing term (D) are automatically
      estimated from the data. For details of the ARIMA model hyperparameters, refer to
      https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.ARIMA.html#pmdarima.arima.ARIMA.
  
      :param p: hyperparameter p for the ARIMA model.
      :param q: hyperparameter q for the ARIMA model.
      :param seasonality_mode: hyperparameter seasonality_mode for the ARIMA model.
      :param P: hyperparameter P for the ARIMA model.
      :param Q: hyperparameter Q for the ARIMA model.
      :param m: hyperparameter m for the ARIMA model.
      :param metric: the metric for validation and evaluation. For regression, we support
          Mean Squared Error: ("mean_squared_error", "MSE" or "mse"),
          Mean Absolute Error: ("mean_absolute_error","MAE" or "mae"),
          Mean Absolute Percentage Error: ("mean_absolute_percentage_error", "MAPE", "mape")
          Cosine Proximity: ("cosine_proximity", "cosine")
      """
      self.model_config = {
          "p": p,
          "q": q,
          "seasonality_mode": seasonality_mode,
          "P": P,
          "Q": Q,
          "m": m,
          "metric": metric,
      }
      self.internal = ARIMAModel()
  
      super().__init__()
  
file_path: BigDL/python/chronos/src/bigdl/chronos/forecaster/arima_forecaster.py
imports: from bigdl.chronos.forecaster.abstract import Forecaster
  from bigdl.chronos.model.arima import ARIMAModel
label_desc: Use image node_id 1 to create a new ARIMAForecaster object from inherited base classes: Forecaster with example: obj = ARIMAForecaster(p, q, seasonality_mode, P, Q, m, metric)