Precise Average Run Length of an Exponentially Weighted Moving Average Control Chart for Time Series Model

Authors

  • Suvimol Phanyaem Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

Keywords:

Average run length, SARX model, explicit formulas, numerical integral equation

Abstract

This study aims to develop a precise formula for calculating the average run length in the context of an exponentially weighted moving average (EWMA) control chart, specifically in the presence of a seasonal autoregressive with exogenous variable (SARX(P,r)L) model. The research also introduces a novel method for estimating the average run length using numerical integral equations, facilitating a comparison between the outcomes derived from the formula and those obtained through the numerical integral equation method. Additionally, control charts are applied to real-world data across diverse domains. The explicit formula is evaluated based on the absolute percentage difference and CPU time. The results show that the average run length calculated using the proposed method precisely corresponds to the findings from the numerical integral equation method. In addition, it’s important to mention that the explicit formulas demonstrated a significant improvement in computational efficiency, requiring much fewer computations than the NIE approach.

References

Areepong Y. An integral equation approach for analysis of control charts. PhD Thesis. Australia: University of Technology; 2009.

Areepong A, Sunthornwat R. EWMA control chart based on its first hitting time and coronavirus alert levels for monitoring symmetric COVID-19 cases. Asian Pac J of Trop Med. 2021; 14(8): 364-374.

Busaba J, Sukparungsee S, Areepong Y. Numerical approximations of average run length for AR(1) on exponential CUSUM. IMECS2012: Proceedings of the International MultiConference of Engineers and Computer Scientists; 2012 Mar 14-16; Hong Kong. 2012.

Champ CW, Rigdon SE. A comparison of the markov chain and the integral equation approaches for evaluating the run length distribution of quality control charts. Commun Stat Simul Comput. 1991; 20(1): 191-204.

Crowder SV. A simple method for studying run-length distributions of exponentially weighted moving average charts. Technometrics. 1987; 29(4): 401-407.

Lucas JM, Saccucci MS. Exponentially weighted moving average control schemes: properties and enhancements. Technometrics. 1990; 32(1): 1-12.

Mititelu G, Areepong Y, Sukparungsee S, Novikov AA. Explicit analytical solutions for the average run length of CUSUM and EWMA charts. East West J Math. 2010; Special edition: 253-265.

Page ES. Continuous inspection schemes. Biometrika. 1954; 41(1/2): 100-115.

Peerajit W. Statistical design of a one-sided CUSUM control chart to detect a mean shift in a FIMAX model with underlying exponential white noise. Thail Stat. 2023; 21(2): 397-420.

Petcharat K, Areepong Y, Sukparungsee S. Exact solution of average run length of EWMA chart for MA(q) processes. Far East J Math Sci. 2013; 78(2): 291-300.

Petcharat K. The effectiveness of CUSUM control chart for trend stationary seasonal autocorrelated data. Thail Stat. 2022; 20(2): 475-488.

Phanyaem S. Explicit formulas and numerical integral equation of ARL for SARX(P,r)L model based on CUSUM chart. Math Stat. 2022; 10(1): 88-99.

Phanthuna P, Areepong Y. Analytical solutions of ARL for SAR(p)L model on a modified EWMA chart. Math Stat. 2021; 9(5): 685-696.

Phanthuna P, Areepong Y, Sukparungsee S. Detection capability of the modified EWMA chart for the trend stationary AR(1) Model. Thail Stat. 2021; 19(1): 69-81.

Roberts SW. Control chart tests based on geometric moving average. Technometrics. 1959; 1(3): 239-250.

Shewhart WA. Economic control of quality of manufactured product. New York: D. Van Nostrand Company, Inc; 1931.

Silpakob K, Areepong Y, Sukparungsee S, Sunthornwat R. Explicit analytical solutions for the average run length of modified EWMA control chart for ARX(p,r) processes. Songklanakarin J Sci Technol. 2021; 43(5): 1414-1427.

Sunthornwat R, Areepong Y, Sukparungsee S. Average run length with a practical investigation of estimating parameters of the EWMA control chart on the long memory AFRIMA process. Thail Stat. 2018; 16(2): 190-202.

Vanbrackle LN, Reynolds MR. EVVMA and CUSUM control charts in the presence of correlation. Commun Stat Simul Comput. 1997; 26(3): 979-1008.

Downloads

Published

2024-09-29

How to Cite

Phanyaem, S. . (2024). Precise Average Run Length of an Exponentially Weighted Moving Average Control Chart for Time Series Model. Thailand Statistician, 22(4), 909–925. Retrieved from https://ph02.tci-thaijo.org/index.php/thaistat/article/view/256080

Issue

Section

Articles