def __init__(self, time_series, baseline_time_series=None):
"""
Initializer
:param TimeSeries time_series: a TimeSeries object.
:param TimeSeries baseline_time_series: baseline TimeSeries.
"""
self.exp_avg_detector = ExpAvgDetector(time_series,
baseline_time_series)
self.derivative_detector = DerivativeDetector(time_series,
baseline_time_series)
class DefaultDetector(AnomalyDetectorAlgorithm):
"""
Default detector.
Not configurable.
"""
def __init__(self, time_series, baseline_time_series=None):
"""
Initializer
:param TimeSeries time_series: a TimeSeries object.
:param TimeSeries baseline_time_series: baseline TimeSeries.
"""
self.exp_avg_detector = ExpAvgDetector(time_series,
baseline_time_series)
self.derivative_detector = DerivativeDetector(time_series,
baseline_time_series)
def _set_scores(self):
"""
Set anomaly scores using a weighted sum.
"""
anom_scores_ema = self.exp_avg_detector.run()
anom_scores_deri = self.derivative_detector.run()
anom_scores = {}
for timestamp in anom_scores_ema.timestamps:
# Compute a weighted anomaly score.
anom_scores[timestamp] = max(
anom_scores_ema[timestamp],
anom_scores_ema[timestamp] * DEFAULT_DETECTOR_EMA_WEIGHT +
anom_scores_deri[timestamp] *
(1 - DEFAULT_DETECTOR_EMA_WEIGHT))
# If ema score is significant enough, take the bigger one of the weighted score and deri score.
if anom_scores_ema[timestamp] > DEFAULT_DETECTOR_EMA_SIGNIFICANT:
anom_scores[timestamp] = max(anom_scores[timestamp],
anom_scores_deri[timestamp])
self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))
class DefaultDetector(AnomalyDetectorAlgorithm):
"""
Default detector.
Not configurable.
"""
def __init__(self, time_series, baseline_time_series=None):
"""
Initializer
:param TimeSeries time_series: a TimeSeries object.
:param TimeSeries baseline_time_series: baseline TimeSeries.
"""
self.exp_avg_detector = ExpAvgDetector(time_series, baseline_time_series)
self.derivative_detector = DerivativeDetector(time_series, baseline_time_series)
def _set_scores(self):
"""
Set anomaly scores using a weighted sum.
"""
anom_scores_ema = self.exp_avg_detector.run()
anom_scores_deri = self.derivative_detector.run()
anom_scores = {}
for timestamp in anom_scores_ema.timestamps:
# Compute a weighted anomaly score.
anom_scores[timestamp] = max(anom_scores_ema[timestamp],
anom_scores_ema[timestamp] * DEFAULT_DETECTOR_EMA_WEIGHT + anom_scores_deri[timestamp] * (1 - DEFAULT_DETECTOR_EMA_WEIGHT))
# If ema score is significant enough, take the bigger one of the weighted score and deri score.
if anom_scores_ema[timestamp] > DEFAULT_DETECTOR_EMA_SIGNIFICANT:
anom_scores[timestamp] = max(anom_scores[timestamp], anom_scores_deri[timestamp])
self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))
def __init__(self, time_series, baseline_time_series=None): """ Initializer :param TimeSeries time_series: a TimeSeries object. :param TimeSeries baseline_time_series: baseline TimeSeries. """ self.exp_avg_detector = ExpAvgDetector(time_series, baseline_time_series) self.derivative_detector = DerivativeDetector(time_series, baseline_time_series)
