[ML] Reduce false positives for the periodic component test for anomaly detection #1177
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valeriy42
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May 4, 2020
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| double p{(softLessThan(v, 1.0, 0.2) && | ||
| softGreaterThan(summary.s_R, summary.s_AutocorrelationThreshold, 0.1) && | ||
| softGreaterThan(summary.s_DF / DFmin[0], 1.0, 0.2) && | ||
| softLessThan(summary.s_TrendSegments, 0.0, 0.3) && | ||
| softLessThan(summary.s_ScaleSegments, 0.0, 0.3)) |
|
retest |
tveasey
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While investigating changes in our QA suite from #1158, I noticed, particularly for sparse data, the periodicity test generates false positives.
These turn out to be because of how we account for piecewise scaling of the periodic component. We allow piecewise linear scaling of the periodic component in the time window we test. However, we didn't directly impose a limit on the minimum number of repeats we require per scale. This change requires (as with unscaled periodicity testing) that we have at least three repeats per scale selected. I also took the opportunity to slightly rework the combination of soft conditions we check to make it clearer what is happening.
The downside of detecting periodicity when none is present is that the model is generally less robust to anomalous behaviour. Also, the periodic component tends to be unstable. Both these effects tend to lead to poor estimation of anomaly severity: in particular, less significant anomalies are scored more highly than more significant anomalies by chance because of model instability.