class_docstr: Calculates time series features (e.g. year, month, hour etc.) of the input time series
  
  Parameters
  ----------
  time_col : string, default=TIME_COL
      issues warning if fraction of nulls is above this value
  
  Attributes
  ----------
  origin_for_time_vars : float (e.g. 2019.2)
      sets default origin so that "ct1" feature from `build_time_features_df` starts at 0 on start date of fitted data
class_docstr_tok: ["Calculates","time","series","features","(","e.g",".","year",",","month",",","hour","etc",".",")","of","the","input","time","series","Parameters","--","--","--","--","--","time_col",":","string",",","default=TIME_COL","issues","warning","if","fraction","of","nulls","is","above","this","value","Attributes","--","--","--","--","--","origin_for_time_vars",":","float","(","e.g",".","2019.2",")","sets","default","origin","so","that","``","ct1","''","feature","from","`","build_time_features_df","`","starts","at","0","on","start","date","of","fitted","data"]
docstr: Sets the time origin for input time series
docstr_tok: ["Sets","the","time","origin","for","input","time","series"]
code: def fit(self, X, y=None):
      """Sets the time origin for input time series"""
      assert isinstance(X, pd.DataFrame)
      dt = X[self.time_col]
      self.origin_for_time_vars = convert_date_to_continuous_time(dt[0])
      return self
  
file_path: greykite/greykite/sklearn/transform/build_timeseries_features_transformer.py
imports: import pandas
  from sklearn.base import BaseEstimator
  from sklearn.base import TransformerMixin
  from sklearn.exceptions import NotFittedError
  from greykite.common.constants import TIME_COL
  from greykite.common.features.timeseries_features import build_time_features_df
  from greykite.common.features.timeseries_features import convert_date_to_continuous_time
label_desc: Use image node_id 2 for calling the BuildTimeseriesFeaturesTransformer obj's underlying member method code with example usage: obj.fit(X, y) and returns: self