Towards Novel Statistical Methods for Anomaly Detection in Industrial Processes-Reference-Cited by-同舟云学术

Towards Novel Statistical Methods for Anomaly Detection in Industrial Processes

Published:2023-04-15 Issue: Volume: Page:
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Container-title:Companion of the 2023 ACM/SPEC International Conference on Performance Engineering
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Author:

Tonini Simone¹^ORCID,Barsacchi Fernando²^ORCID,Chiaromonte Francesca³^ORCID,Licari Daniele¹^ORCID,Vandin Andrea⁴^ORCID

Affiliation:

1. Sant'Anna School of Advanced Studies, Pisa, Italy

2. A. Celli Group, Porcari, Italy

3. Sant'Anna School of Advanced Studies & The Pennsylvania State University, Pisa, Italy

4. Sant'Anna School of Advanced Studies & DTU Technical University of Denmark, Pisa, Italy

Funder

Regione Toscana

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3578245.3585036

Reference19 articles.

1. Springer Berlin Heidelberg, Berlin;Alsmeyer G.;Heidelberg,2011

2. Blázqez-García , A. , Conde , A. , Mori , U. , and Lozano , J. A . A review on outlier/anomaly detection in time series data. ACM Computing Surveys (CSUR) 54, 3 ( 2021 ), 1--33. Blázqez-García, A., Conde, A., Mori, U., and Lozano, J. A. A review on outlier/anomaly detection in time series data. ACM Computing Surveys (CSUR) 54, 3 (2021), 1--33.

3. Multivariate outlier detection based on a robust Mahalanobis distance with shrinkage estimators

4. Series C (Applied Statistics) 29, 3;Campbell N. A.,1980

5. Choi , K. , Yi , J. , Park , C. , and Yoon , S . Deep learning for anomaly detection in time-series data: review, analysis, and guidelines . IEEE Access ( 2021 ). Choi, K., Yi, J., Park, C., and Yoon, S. Deep learning for anomaly detection in time-series data: review, analysis, and guidelines. IEEE Access (2021).