def __init__(self, time_series, baseline_time_series,<EOL>percent_threshold_upper=None, percent_threshold_lower=None,<EOL>offset=<NUM_LIT:0.0>, scan_window=None, confidence=<NUM_LIT>):
super(SignTest, self).__init__(self.__class__.__name__, time_series, baseline_time_series)<EOL>if percent_threshold_upper is None and percent_threshold_lower is None:<EOL><INDENT>raise exceptions.RequiredParametersNotPassed('<STR_LIT>')<EOL><DEDENT>if percent_threshold_upper is not None and percent_threshold_lower is not None:<EOL><INDENT>raise exceptions.RequiredParametersNotPassed('<STR_LIT>')<EOL><DEDENT>if not scan_window:<EOL><INDENT>raise exceptions.RequiredParametersNotPassed('<STR_LIT>')<EOL><DEDENT>self.scan_window = scan_window<EOL>self.confidence = confidence<EOL>self.offset = offset<EOL>self.percent_threshold = percent_threshold_upper if percent_threshold_upper is not None else percent_threshold_lower<EOL>self.scale = <NUM_LIT:1> if percent_threshold_upper is not None else -<NUM_LIT:1><EOL>
:param time_series: current time series
:param baseline_time_series: baseline time series
:param percent_threshold_upper: If time_series is larger than baseline_time_series by this percent, then its
a candidate for an anomaly
:param percent_threshold_lower: If time_series is smaller than baseline_time_series by this percent, then its
a candidate for an anomaly. Percent_threshold_lower should be a negative number to work as intended.
:param offset: baseline will be adjusted by this amount prior to computing percentage
                         TimeSeries > offset +    (1 + percent_threshold_upper/100) * Baseline
                         or for lower value
                         TimeSeries < offset +    (1 + percent_threshold_lower/100) * Baseline
:param scan_window: number of data points to evaluate for anomalies default of 24 = 5 minute period for 2 hours
:param confidence: Confidence to use for determining anomaly, default is 0.01
:return: