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)
class_docstr_tok: ["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",")"]
docstr: Fit(Train) the forecaster.
  
  :param data: A 1-D numpy array as the training data
  :param validation_data: A 1-D numpy array as the evaluation data
docstr_tok: ["Fit","(","Train",")","the","forecaster",".",":","param","data",":","A","1-D","numpy","array","as","the","training","data",":","param","validation_data",":","A","1-D","numpy","array","as","the","evaluation","data"]
code: def fit(self, data, validation_data):
      """
      Fit(Train) the forecaster.
  
      :param data: A 1-D numpy array as the training data
      :param validation_data: A 1-D numpy array as the evaluation data
      """
      self._check_data(data, validation_data)
      data = data.reshape(-1, 1)
      # validation_data = validation_data.reshape(-1, 1)
      return self.internal.fit_eval(
          data=data,
          validation_data=validation_data,
          **self.model_config,
      )
  
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 2 for calling the ARIMAForecaster obj's underlying member method code with example usage: obj.fit(data, validation_data) and returns: self