def _compute_anom_score(self, lag_window_points, point):
"""
Compute anomaly score for a single data point.
Anomaly score for a single data point(t,v) equals: abs(v - ema(lagging window)).
:param list lag_window_points: values in the lagging window.
:param float point: data point value.
:return float: the anomaly score.
"""
ema = utils.compute_ema(self.smoothing_factor, lag_window_points)[-1]
return abs(point - ema)
def _compute_anom_score(self, lag_window_points, point): """ Compute anomaly score for a single data point. Anomaly score for a single data point(t,v) equals: abs(v - ema(lagging window)). :param list lag_window_points: values in the lagging window. :param float point: data point value. :return float: the anomaly score. """ ema = utils.compute_ema(self.smoothing_factor, lag_window_points)[-1] return abs(point - ema)
def _compute_anom_data_decay_all(self):
"""
Compute anomaly scores using a lagging window covering all the data points before.
"""
anom_scores = {}
values = self.time_series.values
ema = utils.compute_ema(self.smoothing_factor, values)
stdev = numpy.std(values)
for i, (timestamp, value) in enumerate(self.time_series_items):
anom_score = abs((value - ema[i]) / stdev) if stdev else value - ema[i]
anom_scores[timestamp] = anom_score
self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))
def _compute_anom_data_decay_all(self):
"""
Compute anomaly scores using a lagging window covering all the data points before.
"""
anom_scores = {}
values = self.time_series.values
ema = utils.compute_ema(self.smoothing_factor, values)
stdev = numpy.std(values)
for i, (timestamp, value) in enumerate(self.time_series_items):
anom_score = abs((value - ema[i]) / stdev) if stdev else value - ema[i]
anom_scores[timestamp] = anom_score
self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))
def _compute_anom_data_decay_all(self):
"""
Compute anomaly scores using a lagging window covering all the data points before.
"""
anom_scores = dict()
values = self.time_series.values
ema = utils.compute_ema(self.smoothing_factor, values)
stdev = numpy.std(values)
for (timestamp, value) in self.time_series.iteritems():
index = self.time_series.timestamps.index(timestamp)
anom_score = abs((value - ema[index]) / stdev) if stdev else value - ema[index]
anom_scores[timestamp] = anom_score
self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))
def _set_scores(self):
"""
Compute anomaly scores for the time series.
"""
anom_scores = {}
self._compute_derivatives()
derivatives_ema = utils.compute_ema(self.smoothing_factor, self.derivatives)
for i, (timestamp, value) in enumerate(self.time_series_items):
anom_scores[timestamp] = abs(self.derivatives[i] - derivatives_ema[i])
stdev = numpy.std(anom_scores.values())
if stdev:
for timestamp in anom_scores.keys():
anom_scores[timestamp] /= stdev
self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))
def _set_scores(self):
"""
Compute anomaly scores for the time series.
"""
anom_scores = {}
self._compute_derivatives()
derivatives_ema = utils.compute_ema(self.smoothing_factor, self.derivatives)
for i, (timestamp, value) in enumerate(self.time_series_items):
anom_scores[timestamp] = abs(self.derivatives[i] - derivatives_ema[i])
stdev = numpy.std(list(anom_scores.values()))
if stdev:
for timestamp in anom_scores.keys():
anom_scores[timestamp] /= stdev
self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))
def _set_scores(self):
"""
Compute anomaly scores for the time series.
"""
anom_scores = dict()
self._compute_derivatives()
derivatives_ema = utils.compute_ema(self.smoothing_factor, self.derivatives)
for (timestamp, value) in self.time_series.iteritems():
index = self.time_series.timestamps.index(timestamp)
anom_scores[timestamp] = abs(self.derivatives[index] - derivatives_ema[index])
stdev = numpy.std(anom_scores.values())
if stdev:
for timestamp in anom_scores.keys():
anom_scores[timestamp] /= stdev
self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))
