Anomaly Detection for Compositional Data using VSI MEWMA control chart
Abstract
In recent years, the monitoring of compositional data using control charts has been investigated in the Statistical Process Control field. In this study, we will design a Phase II Multivariate Exponentially Weighted Moving Average (MEWMA) control chart with variable sampling intervals to monitor compositional data based on isometric log-ratio transformation. The Average Time to Signal will be computed based on the Markov chain approach to investigate the performance of proposed chart. We also propose an optimal procedure to obtain the optimal control limit, smoothing constant, and out-of-control Average Time to Signal for different shift sizes and short sampling intervals. The performance of proposed chart in comparison with the standard MEWMA chart for monitoring compositional data is also provided. Finally, we end the paper with a conclusion and some recommendations for future research.
Keywords
Cite
@article{arxiv.2203.15438,
title = {Anomaly Detection for Compositional Data using VSI MEWMA control chart},
author = {Thi Thuy Van Nguyen and Cédric Heuchenne and Kim Phuc Tran},
journal= {arXiv preprint arXiv:2203.15438},
year = {2022}
}
Comments
This paper was submitted to the "10th IFAC Conference on Manufacturing Modelling, Management and Control, Nantes, France, June 22-24, 2022" on 14/02/2022