Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection

• URL: http://arxiv.org/abs/2201.04792v1
• Date: Thu, 13 Jan 2022 05:12:09 GMT
• Title: Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection
• Authors: Lan Wang, Yusan Lin, Yuhang Wu, Huiyuan Chen, Fei Wang, Hao Yang
• Abstract summary: We present FMUAD - a Forecast-based, Multi-aspect, Unsupervised Anomaly Detection framework. It captures the signature traits of anomaly types - spatial change, temporal change and correlation change - with independent modules. Experiments show it consistently outperforms other state-of-the-art forecast-based anomaly detectors.
• Score: 35.5539484341082
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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