class_docstr: A pipeline for time series forecasting.
docstr: Evaluation
  
  :param input_df: the input dataframe
  :param metrics: the evaluation metrics
  :param multioutput: output mode of multiple output, whether to aggregate
  :return: the evaluation results
docstr_tok: ["Evaluation",":","param","input_df",":","the","input","dataframe",":","param","metrics",":","the","evaluation","metrics",":","param","multioutput",":","output","mode","of","multiple","output",",","whether","to","aggregate",":","return",":","the","evaluation","results"]
code: def evaluate(
      self, input_df, metrics=["mse"], multioutput="raw_values"
  ):
      """
      Evaluation
  
      :param input_df: the input dataframe
      :param metrics: the evaluation metrics
      :param multioutput: output mode of multiple output, whether to aggregate
      :return: the evaluation results
      """
      return self.internal.evaluate(input_df, metrics, multioutput)
  
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 6 for calling the TSPipeline obj's underlying member method code with example usage: obj.evaluate(input_df, metrics, multioutput) and returns: self