mlsanityData to 14 Oct 2026

Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection

arXiv 2308.08915 ↗First posted 17 Aug 2023cs.LG cs.AIcited by 5

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Abstract (arXiv)

Massive key performance indicators (KPIs) are monitored as multivariate time series data (MTS) to ensure the reliability of the software applications and service system. Accurately detecting the abnormality of MTS is very critical for subsequent fault elimination. The scarcity of anomalies and manual labeling has led to the development of various self-supervised MTS anomaly detection (AD) methods, which optimize an overall objective/loss encompassing all metrics' regression objectives/losses. However, our empirical study uncovers the prevalence of conflicts among metrics' regression objectives, causing MTS models to grapple with different losses. This critical aspect significantly impacts detection performance but has been overlooked in existing approaches. To address this problem, by mimicking the design of multi-gate mixture-of-experts (MMoE), we introduce CAD, a Conflict-aware multivariate KPI Anomaly Detection algorithm. CAD offers an exclusive structure for each metric to mitigate potential conflicts while fostering inter-metric promotions. Upon thorough investigation, we find that the poor performance of vanilla MMoE mainly comes from the input-output misalignment settings of MTS formulation and convergence issues arising from expansive tasks. To address these challenges, we propose a straightforward yet effective task-oriented metric selection and p&s (personalized and shared) gating mechanism, which establishes CAD as the first practicable multi-task learning (MTL) based MTS AD model. Evaluations on multiple public datasets reveal that CAD obtains an average F1-score of 0.943 across three public datasets, notably outperforming state-of-the-art methods. Our code is accessible at https://github.com/dawnvince/MTS_CAD.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 14 of its 30 references that are in the map. First posted on arXiv 17 Aug 2023.

Builds on

14 of 30 references.

References · 14

  1. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  2. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
  3. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  4. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 410
  5. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 394
  6. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  7. 2013Learning Factored Representations in a Deep Mixture of Expertscited by 278
  8. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 224
  9. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 215
  10. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 214
  11. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  12. 2019Time-Series Anomaly Detection Service at Microsoftcited by 165
2 more
  1. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 131
  2. 2017Attend and Diagnose: Clinical Time Series Analysis using Attention Modelscited by 105

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.

  1. 2024Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspectivecited by 24
  2. 2024TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Modelscited by 23
  3. 2025When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Seriescited by 7
  4. 2025CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environmentscited by 0
  5. 2026CARE: A Cascaded Framework for Efficient and Reliable Time Series Anomaly Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers
  2. 2023MadSGM: Multivariate Anomaly Detection with Score-based Generative Models
  3. 2026Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers
  4. 2026Evaluating Large Language Models for Time Series Anomaly Detection in Aerospace Software
  5. 2025Forecast2Anomaly (F2A): Adapting Multivariate Time Series Foundation Models for Anomaly Prediction
  6. 2025Can Multimodal LLMs Perform Time Series Anomaly Detection?
  7. 2026Have an LLM Write Your Anomaly Detector: Autonomous Discovery of Compact, Interpretable Detectors for Time Series
  8. 2024Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection