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

被引：0

作者：

Botterman, Hong-Lan ^{[1
]}

Roussel, Julien ^{[1
]}

Morzadec, Thomas ^{[1
]}

Jabbari, Ali ^{[1
]}

Brunel, Nicolas ^{[1
]}

机构：

[1] Quantmetry, 52 Rue Anjou, F-75008 Paris, France

来源：

MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE, LOD 2022, PT II | 2023年 / 13811卷

关键词：

Robust principal component analysis; Anomaly detection; Data imputation; Regularisation; Adaptive estimation; SPARSE;

D O I：

10.1007/978-3-031-25891-6_21

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：281 / 295

页数：15

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