Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection. (arXiv:2201.04792v1 [cs.LG])

Today's cyber-world is vastly multivariate. Metrics collected at extreme
varieties demand multivariate algorithms to properly detect anomalies. However,
forecast-based algorithms, as widely proven approaches, often perform
sub-optimally or inconsistently across datasets. A key common issue is they
strive to be one-size-fits-all but anomalies are distinctive in nature. We
propose a method that tailors to such distinction. Presenting FMUAD - a
Forecast-based, Multi-aspect, Unsupervised Anomaly Detection framework. FMUAD
explicitly and separately captures the signature traits of anomaly types -
spatial change, …

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