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",")"]
code: def _check_data(self, data, validation_data):
      from bigdl.nano.utils.common import invalidInputError
  
      invalidInputError(
          data.ndim == 1,
          "data should be an 1-D array),"
          "Got data dimension of {}.".format(data.ndim),
      )
      invalidInputError(
          validation_data.ndim == 1,
          "validation_data should be an 1-D array),"
          "Got validation_data dimension of {}.".format(
              validation_data.ndim
          ),
      )
  
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 3 for calling the ARIMAForecaster obj's underlying member method code with example usage: obj._check_data(data, validation_data) without return types