class_docstr: TimeSequence feature engineering
docstr: Constructor.
  :param future_seq_len: the future sequence length to be predicted
  :dt_col: name of datetime column in the input data frame
  :target_col: name of target column in the input data frame
  :extra_features_col: name of extra feature columns that needs to predict the target column.
  :param drop_missing: whether to drop missing values in the curve, if this is set to False,
                       an error will be reported if missing values are found. If True, will
                       drop the missing values and won't throw errors.
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code: def __init__(
      self,
      future_seq_len=1,
      dt_col="datetime",
      target_col=["value"],
      extra_features_col=None,
      drop_missing=True,
      time_features=True,
  ):
      """
      Constructor.
      :param future_seq_len: the future sequence length to be predicted
      :dt_col: name of datetime column in the input data frame
      :target_col: name of target column in the input data frame
      :extra_features_col: name of extra feature columns that needs to predict the target column.
      :param drop_missing: whether to drop missing values in the curve, if this is set to False,
                           an error will be reported if missing values are found. If True, will
                           drop the missing values and won't throw errors.
      """
      # self.scaler = MinMaxScaler()
      self.scaler = StandardScaler()
      self.config = None
      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.feature_data = None
      self.drop_missing = drop_missing
      self.generate_feature_list = None
      self.past_seq_len = None
      self.future_seq_len = future_seq_len
      self.time_features = time_features
  
imports: from bigdl.chronos.autots.deprecated.feature.utils import save_config
  from bigdl.chronos.autots.deprecated.feature.abstract import BaseFeatureTransformer
  from bigdl.chronos.utils import deprecated
  import sklearn
  from sklearn.preprocessing import StandardScaler
  import pandas
  import numpy
  import json
  from packaging import version
label_desc: Use image node_id 1 to create a new TimeSequenceFeatureTransformer object from inherited base classes: BaseFeatureTransformer with example: obj = TimeSequenceFeatureTransformer(future_seq_len, dt_col, target_col, extra_features_col, drop_missing, time_features)