class_docstr: Trains a model that predicts future time sequence from past sequence.
  Past sequence should be > 1. Future sequence can be > 1.
  For example, predict the next 2 data points from past 5 data points.
  Output have only one target value (a scalar) for each data point in the sequence.
  Input can have more than one features (value plus several features)
  Example usage:
      tsp = TimeSequencePredictor()
      tsp.fit(input_df)
      result = tsp.predict(test_df)
code: def _check_missing_col(self, df):
      cols_list = [self.dt_col] + self.target_col
      if self.extra_features_col is not None:
          if not isinstance(self.extra_features_col, (list,)):
              invalidInputError(
                  False,
                  "extra_features_col needs to be either None or a list",
              )
          cols_list.extend(self.extra_features_col)
  
      missing_cols = set(cols_list) - set(df.columns)
      if len(missing_cols) != 0:
          invalidInputError(
              False,
              "Missing Columns in the input data frame:"
              + ",".join(list(missing_cols)),
          )
  
file_path: BigDL/python/chronos/src/bigdl/chronos/autots/deprecated/regression/time_sequence_predictor.py
imports: from bigdl.chronos.autots.deprecated.regression.base_predictor import BasePredictor
  from bigdl.chronos.utils import deprecated
label_desc: Use image node_id 3 for calling the TimeSequencePredictor obj's underlying member method code with example usage: obj._check_missing_col(df) without return types