mlsanityData to 13 Oct 2026

Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series

arXiv 2205.15548 ↗First posted 31 May 2022stat.ML cs.AI cs.LGcited by 0

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Abstract (arXiv)

This paper presents a novel, closed-form, and data/computation efficient online anomaly detection algorithm for time-series data. The proposed method, dubbed RPE, is a window-based method and in sharp contrast to the existing window-based methods, it is robust to the presence of anomalies in its window and it can distinguish the anomalies in time-stamp level. RPE leverages the linear structure of the trajectory matrix of the time-series and employs a robust projection step which makes the algorithm able to handle the presence of multiple arbitrarily large anomalies in its window. A closed-form/non-iterative algorithm for the robust projection step is provided and it is proved that it can identify the corrupted time-stamps. RPE is a great candidate for the applications where a large training data is not available which is the common scenario in the area of time-series. An extensive set of numerical experiments show that RPE can outperform the existing approaches with a notable margin.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 11 of its 38 references that are in the map. First posted on arXiv 31 May 2022.

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References · 11

  1. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 408
  2. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 223
  3. 2020Multivariate Time-series Anomaly Detection via Graph Attention Networkcited by 165
  4. 2019Time-Series Anomaly Detection Service at Microsoftcited by 163
  5. 2020Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Artcited by 56
  6. 2014Fast, Robust and Non-convex Subspace Recoverycited by 20
  7. 2022Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detectioncited by 16
  8. 2016Coherence Pursuit: Fast, Simple, and Robust Principal Component Analysiscited by 13
  9. 2018Structured low-rank matrix completion for forecasting in time series analysiscited by 6
  10. 2015Online Matrix Completion and Online Robust PCAcited by 3
  11. 2015High Dimensional Low Rank plus Sparse Matrix Decompositioncited by 1

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