mlsanityData to 7 Oct 2026

Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series

arXiv 2208.01998 ↗First posted 3 Aug 2022stat.ML cs.LGcited by 2

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Abstract (arXiv)

We propose a robust principal component analysis (RPCA) framework to recover low-rank and sparse matrices from temporal observations. We develop an online version of the batch temporal algorithm in order to process larger datasets or streaming data. We empirically compare the proposed approaches with different RPCA frameworks and show their effectiveness in practical situations.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 3 of its 29 references that are in the map. First posted on arXiv 3 Aug 2022.

Builds on

3 of 29 references.

References · 3

  1. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  2. 2020A review on outlier/anomaly detection in time series datacited by 187
  3. 2015Robust Principal Component Analysis on Graphscited by 10

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Citing papers per year: 2025: 2.

2 papers.

  1. 2025Finding Outliers in a Haystack: Anomaly Detection for Large Pointcloud Scenescited by 1
  2. 2025Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Seriescited by 1