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Performance comparison of procedures for monitoring autocorrelated processes based on classification and forecasting
Journal of the Korean Data & Information Science Society 2023;34:775-89
Published online September 30, 2023;
© 2023 Korean Data and Information Science Society.

Pyoungjin Ji1 · Jaeheon Lee2

12Department of Applied Statistics, Chung-Ang University
Correspondence to: 1 Graduate student, Department of Applied Statistics, Chung-Ang University, Seoul 06974, Korea.
2 Corresponding author: Professor, Department of Applied Statistics, Chung-Ang University, Seoul 06974, Korea. E-mail:
Received July 24, 2023; Revised September 15, 2023; Accepted September 18, 2023.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
There has been extensive research on the procedures for monitoring autocorrelated processes. Among them, the most commonly used approach is to forecast the next observation based on a fitted model, calculate residuals, and apply control charting procedures to the residual data. In this paper, we propose a process monitoring procedure based on a recurrent neural network (RNN) to classify whether the process is in control or out of control. The performance of this procedure is compared with the forecasting procedure based on a RNN and the traditional residual control charting procedure through simulation study. The results show that the RNN-based classification procedure quickly detects changes in the process level, and the RNN-based forecasting procedure quickly detects changes in the process variance. Additionally, unlike the traditional monitoring procedure, the RNN-based procedures have the advantage that they do not require accurate model fitting for process data.
Keywords : Autocorrelated process, deep learning, residual chart, RNN