mlsanityData to 7 Oct 2026

MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast

arXiv 2410.15997 ↗First posted 21 Oct 2024cs.LGcited by 18

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Abstract (arXiv)

Many methods have been proposed for unsupervised time series anomaly detection. Despite some progress, research on predicting future anomalies is still relatively scarce. Predicting anomalies is particularly challenging due to the diverse reaction time and the lack of labeled data. To address these challenges, we propose MultiRC to integrate reconstructive and contrastive learning for joint learning of anomaly prediction and detection, with multi-scale structure and adaptive dominant period mask to deal with the diverse reaction time. MultiRC also generates negative samples to provide essential training momentum for the anomaly prediction tasks and prevent model degradation. We evaluate seven benchmark datasets from different fields. For both anomaly prediction and detection tasks, MultiRC outperforms existing state-of-the-art methods.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 22 of its 43 references that are in the map. First posted on arXiv 21 Oct 2024.

Builds on

22 of 43 references.

References · 22

  1. 2017Attention Is All You Needcited by 59,932
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10 more
  1. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  2. 2023DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detectioncited by 103
  3. 2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 95
  4. 2022Local Evaluation of Time Series Anomaly Detection Algorithmscited by 79
  5. 2023Soft Contrastive Learning for Time Seriescited by 49
  6. 2021Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Versioncited by 35
  7. 2023MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detectioncited by 26
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 13; 2026: 3.

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  1. 2024DUET: Dual Clustering Enhanced Multivariate Time Series Forecastingcited by 104
  2. 2025TAB: Unified Benchmarking of Time Series Anomaly Detection Methodscited by 58
  3. 2025OFFSET: Segmentation-based Focus Shift Revision for Composed Image Retrievalcited by 40
  4. 2024EasyTime: Time Series Forecasting Made Easycited by 30
  5. 2025ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecastingcited by 8
  6. 2025K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecastingcited by 7
  7. 2025DDUNet: Dual Dynamic U-Net for Highly-Efficient Cloud Segmentationcited by 6
  8. 2025Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Servicescited by 6
  9. 2025AimTS: Augmented Series and Image Contrastive Learning for Time Series Classificationcited by 5
  10. 2025CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image Segmentationcited by 3
  11. 2026Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interactioncited by 3
  12. 2025RED-F: Reconstruction-Elimination based Dual-stream Contrastive Forecasting for Multivariate Time Series Anomaly Predictioncited by 1
6 more
  1. 2025VISTA: Unsupervised 2D Temporal Dependency Representations for Time Series Anomaly Detectioncited by 0
  2. 2025Segregation and Context Aggregation Network for Real-time Cloud Segmentationcited by 0
  3. 2025FADE: Adversarial Concept Erasure in Flow Modelscited by 0
  4. 2025Robust Concept Erasure in Diffusion Models: A Theoretical Perspective on Security and Robustnesscited by 0
  5. 2026Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensemblescited by 0
  6. 2026XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matchingcited by 0