mlsanityData to 14 Oct 2026

Anomaly Prediction: A Novel Approach with Explicit Delay and Horizon

arXiv 2408.04377 ↗First posted 8 Aug 2024cs.LG cs.AIcited by 3

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Abstract (arXiv)

Anomaly detection in time series data is a critical challenge across various domains. Traditional methods typically focus on identifying anomalies in immediate subsequent steps, often underestimating the significance of temporal dynamics such as delay time and horizons of anomalies, which generally require extensive post-analysis. This paper introduces a novel approach for time series anomaly prediction, incorporating temporal information directly into the prediction results. We propose a new dataset specifically designed to evaluate this approach and conduct comprehensive experiments using several state-of-the-art methods. Our results demonstrate the efficacy of our approach in providing timely and accurate anomaly predictions, setting a new benchmark for future research in this field.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 16 of its 50 references that are in the map. First posted on arXiv 8 Aug 2024.

Builds on

16 of 50 references.

References · 16

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  4. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 607
  5. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 394
  6. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 214
  7. 2019Time-Series Anomaly Detection Service at Microsoftcited by 165
  8. 2020COPOD: Copula-Based Outlier Detectioncited by 146
  9. 2015Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmarkcited by 88
  10. 2019U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Stagingcited by 88
  11. 2020Exathlon: A Benchmark for Explainable Anomaly Detection over Time Seriescited by 56
  12. 2024TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Modelscited by 23
4 more
  1. 2022Series2Graph: Graph-based Subsequence Anomaly Detection for Time Seriescited by 13
  2. 2020Event Prediction in the Big Data Era: A Systematic Surveycited by 12
  3. 2024Kernel-U-Net: Multivariate Time Series Forecasting using Custom Kernelscited by 4
  4. 2020Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodescited by 3

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Cited by year

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

3 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. 2025RED-F: Reconstruction-Elimination based Dual-stream Contrastive Forecasting for Multivariate Time Series Anomaly Predictioncited by 2