def __init__(self,
time_series,
baseline_time_series,
percent_threshold_upper=None,
percent_threshold_lower=None,
offset=0.0,
scan_window=None,
confidence=0.01):
"""
: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:
"""
super(SignTest, self).__init__(self.__class__.__name__, time_series,
baseline_time_series)
if percent_threshold_upper is None and percent_threshold_lower is None:
raise exceptions.RequiredParametersNotPassed(
'luminol.algorithms.anomaly_detector_algorithms.sign_test: \
Either percent_threshold_upper or percent_threshold_lower is needed'
)
if percent_threshold_upper is not None and percent_threshold_lower is not None:
raise exceptions.RequiredParametersNotPassed(
'luminol.algorithms.anomaly_detector_algorithms.sign_test: ' +
'Cannot specify both percent_threshold_upper and percent_threshold_lower'
)
if not scan_window:
raise exceptions.RequiredParametersNotPassed(
'luminol.algorithms.anomaly_detector_algorithms.sign_test: ' +
' scan window size needs to be specified')
# make assignements
self.scan_window = scan_window
self.confidence = confidence
self.offset = offset
self.percent_threshold = percent_threshold_upper if percent_threshold_upper is not None else percent_threshold_lower
# scale will transform the time series and baseline
# if we are detecting lower threshold we mirror the data
self.scale = 1 if percent_threshold_upper is not None else -1
def __init__(self, time_series, baseline_time_series, percent_threshold_upper=None, percent_threshold_lower=None):
"""
: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 an anomaly
:param percent_threshold_lower: If time_series is smaller than
baseline_time_series by this percent, then its an anomaly
"""
super(DiffPercentThreshold, self).__init__(self.__class__.__name__, time_series, baseline_time_series)
self.percent_threshold_upper = percent_threshold_upper
self.percent_threshold_lower = percent_threshold_lower
if not self.percent_threshold_upper and not self.percent_threshold_lower:
raise exceptions.RequiredParametersNotPassed(
'luminol.algorithms.anomaly_detector_algorithms.diff_percent_threshold: ' +
'Either percent_threshold_upper or percent_threshold_lower needed')
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')