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 __init__(
      self,
      name="automl",
      logs_dir="~/bigdl_automl_logs",
      future_seq_len=1,
      dt_col="datetime",
      target_col=["value"],
      extra_features_col=None,
      drop_missing=True,
      search_alg=None,
      search_alg_params=None,
      scheduler=None,
      scheduler_params=None,
  ):
      self.pipeline = None
      self.future_seq_len = future_seq_len
      self.dt_col = dt_col
      if isinstance(target_col, str):
          self.target_col = [target_col]
      else:
          self.target_col = target_col
      self.extra_features_col = extra_features_col
      self.drop_missing = drop_missing
      super().__init__(
          name=name,
          logs_dir=logs_dir,
          search_alg=search_alg,
          search_alg_params=search_alg_params,
          scheduler=scheduler,
          scheduler_params=scheduler_params,
      )
  
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 1 to create a new TimeSequencePredictor object from inherited base classes: BasePredictor with example: obj = TimeSequencePredictor(name, logs_dir, future_seq_len, dt_col, target_col, extra_features_col, drop_missing, search_alg, search_alg_params, scheduler, scheduler_params)