class_docstr: The Automated Time Series Forecast Trainer
docstr: fit()
  Fit a time series forecasting pipeline w/ automl
  
  :param train_df: the input dataframe (as pandas.dataframe)
  :param validation_df: the validation dataframe (as pandas.dataframe)
  :param recipe: the configuration of searching
  :param metric: the evaluation metric to optimize
  :param uncertainty: whether to enable uncertainty calculation
                      (will output an uncertainty sigma)
  :param upload_dir: Optional URI to sync training results and checkpoints. We only support
      hdfs URI for now.
  :return a TSPipeline
docstr_tok: ["fit","(",")","Fit","a","time","series","forecasting","pipeline","w\/","automl",":","param","train_df",":","the","input","dataframe","(","as","pandas.dataframe",")",":","param","validation_df",":","the","validation","dataframe","(","as","pandas.dataframe",")",":","param","recipe",":","the","configuration","of","searching",":","param","metric",":","the","evaluation","metric","to","optimize",":","param","uncertainty",":","whether","to","enable","uncertainty","calculation","(","will","output","an","uncertainty","sigma",")",":","param","upload_dir",":","Optional","URI","to","sync","training","results","and","checkpoints",".","We","only","support","hdfs","URI","for","now",".",":","return","a","TSPipeline"]
code: def fit(
      self,
      train_df,
      validation_df=None,
      metric="mse",
      recipe: Recipe = SmokeRecipe(),
      uncertainty: bool = False,
      upload_dir=None,
  ):
      """
      fit()
      Fit a time series forecasting pipeline w/ automl
  
      :param train_df: the input dataframe (as pandas.dataframe)
      :param validation_df: the validation dataframe (as pandas.dataframe)
      :param recipe: the configuration of searching
      :param metric: the evaluation metric to optimize
      :param uncertainty: whether to enable uncertainty calculation
                          (will output an uncertainty sigma)
      :param upload_dir: Optional URI to sync training results and checkpoints. We only support
          hdfs URI for now.
      :return a TSPipeline
      """
      zoo_pipeline = self.internal.fit(
          train_df,
          validation_df,
          metric,
          recipe,
          mc=uncertainty,
          upload_dir=upload_dir,
      )
      ppl = TSPipeline()
      ppl.internal = zoo_pipeline
      return ppl
  
file_path: analytics-zoo/pyzoo/zoo/chronos/autots/deprecated/forecast.py
imports: from zoo.chronos.autots.deprecated.regression.time_sequence_predictor import TimeSequencePredictor
  from zoo.chronos.autots.deprecated.config.recipe import *
  from zoo.chronos.autots.deprecated.pipeline.time_sequence import load_ts_pipeline
  from zoo.chronos.utils import deprecated
label_desc: Use image node_id 2 for calling the AutoTSTrainer obj's underlying member method code with example usage: obj.fit(train_df, validation_df, metric, recipe, uncertainty, upload_dir) and returns: ppl