UNSUPERVISED ANOMALY DETECTION FOR ARBITRARY TIME SERIES

摘要

Examining time series sequences representing performance counters from executing programs can provide significant clues about potential malfunctions, busy periods in terms of traffic on networks, intensive processing cycles and so on. An unsupervised anomaly detector can detect anomalies for any time series. A combination of known techniques from statistics, signal processing and machine learning can be used to identify outliers on unsupervised data, and to capture anomalies like edge detection, spike detection, and pattern error anomalies. Boolean and probabilistic results concerning whether an anomaly was detected can be provided.

主权项

1. A system comprising:
at least one processor: a memory connected to the at least one processor; and at least one module comprising an anomaly detector that when loaded into the at least one processor causes the at least one processor to identify anomalies comprising unexpected values in a time series of performance counters generated by a component by: determining a distribution of data points within the time series; performing pre-processing of the time series; detect anomalies within the time series; and perform post-processing of the detected anomalies.