  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, temporal change and correlation change - with independent
modules. The modules then jointly learn an optimal feature representation,
which is highly flexible and intuitive, unlike most other models in the
category. Extensive experiments show our FMUAD framework consistently
outperforms other state-of-the-art forecast-based anomaly detectors.
