class_docstr: Anomalies are those data points that are above a pre-specified threshold value.
  This algorithm does not take baseline time series.
class_docstr_tok: ["Anomalies","are","those","data","points","that","are","above","a","pre-specified","threshold","value",".","This","algorithm","does","not","take","baseline","time","series","."]
docstr: Initialize algorithm, check all required args are present
  
  :param time_series: The current time series dict to run anomaly detection on
  :param absolute_threshold_value_upper: Time series values above this are considered anomalies
  :param absolute_threshold_value_lower: Time series values below this are considered anomalies
  :param baseline_time_series: A no-op for now
  :return:
docstr_tok: ["Initialize","algorithm",",","check","all","required","args","are","present",":","param","time_series",":","The","current","time","series","dict","to","run","anomaly","detection","on",":","param","absolute_threshold_value_upper",":","Time","series","values","above","this","are","considered","anomalies",":","param","absolute_threshold_value_lower",":","Time","series","values","below","this","are","considered","anomalies",":","param","baseline_time_series",":","A","no-op","for","now",":","return",":"]
code: def __init__(
      self,
      time_series,
      absolute_threshold_value_upper=None,
      absolute_threshold_value_lower=None,
      baseline_time_series=None,
  ):
      """
      Initialize algorithm, check all required args are present
  
      :param time_series: The current time series dict to run anomaly detection on
      :param absolute_threshold_value_upper: Time series values above this are considered anomalies
      :param absolute_threshold_value_lower: Time series values below this are considered anomalies
      :param baseline_time_series: A no-op for now
      :return:
      """
      super(AbsoluteThreshold, self).__init__(
          self.__class__.__name__, time_series, baseline_time_series
      )
      self.absolute_threshold_value_upper = (
          absolute_threshold_value_upper
      )
      self.absolute_threshold_value_lower = (
          absolute_threshold_value_lower
      )
      if (
          not self.absolute_threshold_value_lower
          and not self.absolute_threshold_value_upper
      ):
          raise exceptions.RequiredParametersNotPassed(
              "luminol.algorithms.anomaly_detector_algorithms.absolute_threshold: "
              "Either absolute_threshold_value_upper or "
              "absolute_threshold_value_lower needed"
          )
  
imports: from luminol import exceptions
  from luminol.algorithms.anomaly_detector_algorithms import AnomalyDetectorAlgorithm
  from luminol.modules.time_series import TimeSeries
label_desc: Use image node_id 1 to create a new AbsoluteThreshold object from inherited base classes: AnomalyDetectorAlgorithm with example: obj = AbsoluteThreshold(time_series, absolute_threshold_value_upper, absolute_threshold_value_lower, baseline_time_series)