mlsanityData to 7 Oct 2026

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

arXiv 2201.04792 ↗First posted 13 Jan 2022cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 35 references that are in the map. First posted on arXiv 13 Jan 2022.

Builds on

15 of 35 references.

References · 15

  1. 2017Attention Is All You Needcited by 59,932
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3 more
  1. 2020Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learningcited by 22
  2. 2021SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Seriescited by 8
  3. 2021Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detectioncited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.

2 papers.

  1. 2023Context-aware Domain Adaptation for Time Series Anomaly Detectioncited by 8
  2. 2026Unified Taxonomy for Multivariate Time Series Anomaly Detection using Deep Learningcited by 0