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

arXiv: https://arxiv.org/abs/2208.01998 · HTML: https://mlsanity.com/paper/2208.01998/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 3 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [A review on outlier/anomaly detection in time series data](https://mlsanity.com/paper/2002.04236/) (2020)
- [Robust Principal Component Analysis on Graphs](https://mlsanity.com/paper/1504.06151/) (2015)

## Built on by (most cited first, 2 of 2)

- [Finding Outliers in a Haystack: Anomaly Detection for Large Pointcloud Scenes](https://mlsanity.com/paper/2508.17634/) (2025)
- [Federated Quantum Kernel Learning for Anomaly Detection in Multivariate IoT Time-Series](https://mlsanity.com/paper/2511.02301/) (2025)

## 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.