mlsanityData to 14 Oct 2026

Precursor-of-Anomaly Detection for Irregular Time Series

arXiv 2306.15489 ↗First posted 27 Jun 2023cs.AIcited by 9

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Abstract (arXiv)

Anomaly detection is an important field that aims to identify unexpected patterns or data points, and it is closely related to many real-world problems, particularly to applications in finance, manufacturing, cyber security, and so on. While anomaly detection has been studied extensively in various fields, detecting future anomalies before they occur remains an unexplored territory. In this paper, we present a novel type of anomaly detection, called Precursor-of-Anomaly (PoA) detection. Unlike conventional anomaly detection, which focuses on determining whether a given time series observation is an anomaly or not, PoA detection aims to detect future anomalies before they happen. To solve both problems at the same time, we present a neural controlled differential equation-based neural network and its multi-task learning algorithm. We conduct experiments using 17 baselines and 3 datasets, including regular and irregular time series, and demonstrate that our presented method outperforms the baselines in almost all cases. Our ablation studies also indicate that the multitasking training method significantly enhances the overall performance for both anomaly and PoA detection.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 19 of its 76 references that are in the map. First posted on arXiv 27 Jun 2023.

Builds on

19 of 76 references.

References · 19

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7 more
  1. 2019Deep Semi-Supervised Anomaly Detectioncited by 291
  2. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  3. 2018Efficient GAN-Based Anomaly Detectioncited by 122
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 4.

9 papers.

  1. 2024MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrastcited by 19
  2. 2025When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Seriescited by 7
  3. 2026SC-JEPA: Stabilizing Latent Predictive Learning for Time-Series Anomaly Predictioncited by 4
  4. 2024GDFlow: Anomaly Detection with NCDE-based Normalizing Flow for Advanced Driver Assistance Systemcited by 3
  5. 2025RED-F: Reconstruction-Elimination based Dual-stream Contrastive Forecasting for Multivariate Time Series Anomaly Predictioncited by 2
  6. 2026LLM-Assisted Logic Rule Learning: Scaling Human Expertise for Time Series Anomaly Detectioncited by 2
  7. 2025VISTA: Unsupervised 2D Temporal Dependency Representations for Time Series Anomaly Detectioncited by 0
  8. 2026Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensemblescited by 0
  9. 2026Transformer autoencoder with local attention for sparse and irregular time series with application on risk estimationcited by 0