def _compute_anom_data_decay_all(self):
anom_scores = {}<EOL>values = self.time_series.values<EOL>ema = utils.compute_ema(self.smoothing_factor, values)<EOL>stdev = numpy.std(values)<EOL>for i, (timestamp, value) in enumerate(self.time_series_items):<EOL><INDENT>anom_score = abs((value - ema[i]) / stdev) if stdev else value - ema[i]<EOL>anom_scores[timestamp] = anom_score<EOL><DEDENT>self.anom_scores = TimeSeries(self._denoise_scores(anom_scores))<EOL>
Compute anomaly scores using a lagging window covering all the data points before.