The performance of a modified EWMA control chart for monitoring autocorrelated PM2.5 and carbon monoxide air pollution data

Supharakonsakun, Yadpirun; Areepong, Yupaporn; Sukparungsee, Saowanit

doi:10.7717/peerj.10467

Cited by 11 publications

(12 citation statements)

References 40 publications

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“…( 22) then the absolute percentage relative error named APRE(%) show that Eq. (24). It showed that the ARL values derived from the explicit formulas give results close to those from the NIE method both AR(2) and AR(3) process.…”

Section: Numerical Resultsmentioning

confidence: 65%

“…Karoon et al [23] developed the numerical integral equation (NIE) methods for evaluating the ARL on Extended EWMA chart for AR(p) process. Supharakonsakun et al [24] presented the exact average run length based on explicit formula the observations are from moving average process with exponential white noise for modified EWMA chart. Phanthuna et al [25] proposed the explicit formula for evaluating the ARL on a two-sided modified EWMA chart under the observations of AR(1) process.…”

Section: Introductionmentioning

confidence: 99%

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Exact Run Length Evaluation on Extended EWMA Control Chart for Autoregressive Process

Karoon¹,

Areepong²,

Sukparungsee³

2022

Intelligent Automation &Amp; Soft Computing

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Extended Exponentially Weighted Moving Average (Extended EWMA or EEWMA) control chart is one of the control charts which can quickly detect a small shift. The average run length (ARL) measures the performance of control chart. Due to the derivation of the explicit formulas for ARL on the EEWMA control chart for the autoregressive AR(p) process has not previously been reported. The aim of the article is to derive explicit formulas of ARL using a Fredholm integral equation of the second kind on EEWMA control chart for Autoregressive process, as AR(2) and AR(3) processes with exponential white noise. The accuracy of the solution obtained with the EEWMA control chart was compared to the numerical integral equation (NIE) method and extended to compare performance with Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) control charts. The results show that the ARL obtained by the explicit formula and the NIE method are hardly different but ARL of explicit formula is less the computational (CPU) time than ARL of NIE method. The performance of EEWMA control chart is better than the CUSUM and EWMA control charts for all situations except when the large shift sizes the EEWMA control chart performed as well as the EWMA control chart for AR(2) and AR(3) processes. And then, the EEWMA control chart is also extended to compare efficiency of EEW-MA control chart with various λ. An exponential smoothing parameter of 0.05 is recommended. In addition, the simulation study, and efficacy illustration with real data on new COVID-19 cases in Thailand and Vietnam provided similar results.

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Section: Numerical Resultsmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Exact Run Length Evaluation on Extended EWMA Control Chart for Autoregressive Process

Karoon¹,

Areepong²,

Sukparungsee³

2022

Intelligent Automation &Amp; Soft Computing

Self Cite

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“…charts and examples of their use with correlated data, see Hunter (1986); Lucas and Saccucci (1990); Supharakonsakun et al (2020).…”

Section: Detection Of Trend Deviationsmentioning

confidence: 99%

Online trend estimation and detection of trend deviations in sub-sewershed time series of SARS-CoV-2 RNA measured in wastewater

Ensor,

Schedler,

Sun

et al. 2023

Preprint

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Wastewater surveillance has proven a key public health tool to understand a wide range of community health diseases and has proven to be especially critical to health departments throughout the SARS CoV-2 pandemic. The size of the population served by a wastewater treatment plant (WWTP) may limit the targeted insight about community disease dynamics. To investigate this concern, samples of wastewater were obtained at lift stations upstream of WWTPs within the sewer network. First, an online, semi-automatic time series model is fitted to the weekly measurements of WWTP samples to estimate the viral trend for the community and compared to the time series observations from the lift stations. Second, deviations from the WWTP trend are identified using an Exponentially Weighted Moving Average (EWMA) control chart. The analysis reveals that the lift stations display slightly different dynamics than the larger WWTP, highlighting the more granular insight gleaned from sampling sites which represent smaller populations. Discussion focuses on the use of our methods to support rapid public health decision-making based on additional, targeted samples in times of concern.

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“…Explicit formulas for the exact ARL were provided and their accuracy was compared with the NIE method. Supharakonsakun et al [32] suggested explicit formulas for the ARL with observations from a MA model on the modified EWMA control chart and compared their capability with the same process on the standard EWMA scheme for monitoring PM2.5 and carbon monoxide air pollution data; their results show that the modified EWMA control chart was much better at detecting shifts in the process parameter than the standard EWMA control chart. Supharakonsakun [33] designed the modified EWMA control chart for a seasonal MA model and evaluated its efficacy by using the ARL calculated via explicit formulas and the NIE method.…”

Section: -Literature Reviewmentioning

confidence: 99%

Detection Sensitivity of a Modified EWMA Control Chart with a Time Series Model with Fractionality and Integration

Phanthuna

Areepong

2022

Emerg Sci J

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Among the many statistical process control charts, the modified exponentially weighted moving average (EWMA) control chart has been designed to swiftly detect a small shift in a process parameter. Herein, we propose two explicit formulas for the average run length (ARL) for integrated moving average (IMA) and fractional integrated moving average (FIMA) models combined with the modified EWMA control chart for time series prediction. The application of the suggested control chart procedures depends on the residuals of the IMA and FIMA models. The performance of the control chart with both models is evaluated by using the ARL. Explicit formulas for the ARL for the two models with the modified EWMA statistic are derived and their precision is compared with the numerical integral equation method. The explicit formulas could accurately predict the true ARL while markedly decreasing the computational processing time compared to the numerical integration method. The capabilities of the IMA and FIMA models with the modified EWMA control chart were studied by varying g times the last term and exponential smoothing parameter λ, with the relative mean index being used to evaluate these situations. The results show that the modified EWMA control chart with either model performed better than the original EWMA control chart. Furthermore, the modified EWMA control chart with either model was highly efficient when g increased and λ was small. Two applications involving energy commodity prices are used to illustrate the efficacies of the proposed approaches, the results of which were in accordance with the experimental study. Doi: 10.28991/ESJ-2022-06-05-015 Full Text: PDF

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The performance of a modified EWMA control chart for monitoring autocorrelated PM2.5 and carbon monoxide air pollution data

Cited by 11 publications

References 40 publications

Exact Run Length Evaluation on Extended EWMA Control Chart for Autoregressive Process

Exact Run Length Evaluation on Extended EWMA Control Chart for Autoregressive Process

Online trend estimation and detection of trend deviations in sub-sewershed time series of SARS-CoV-2 RNA measured in wastewater

Detection Sensitivity of a Modified EWMA Control Chart with a Time Series Model with Fractionality and Integration

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