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All Journal Emerging Science Journal
Areepong, Yupaporn
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Environmental Science

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Journal : Emerging Science Journal

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Combining a Moving Average with a Triple EWMA Chart to Improve Detection Performance Saesuntia, Piyatida; Areepong, Yupaporn; Sukparungsee, Saowanit
Emerging Science Journal Vol. 9 No. 5 (2025): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-05-05

Abstract

This article aims to introduce the novel mixed triple exponentially weighted moving average-moving average (MTEM) chart to accurately detect position changes for both symmetric and non-symmetric distributions. The MTEM chart constructs a moving average (MA) structure to filter out fluctuations in the raw data and then applies triple exponential weighting to improve the ability to identify minor shifts. The average run length (ARL) and median run length (MRL), which are run length profiles derived from the Monte Carlo simulation (MC) strategy, were used to compare the performance of the suggested chart with that of MA, EWMA, TEWMA, and mixed moving average-triple exponentially weighted moving average (MMTE) charts. In addition, the expected average run length (EARL) and expected median run length (EMRL) were also used to rate the overall results. Results of the study indicate that the MTEM chart surpasses competitor charts in detecting minor to moderate changes. The MMTE chart responds slightly slower than the proposed chart. Due to its smoothed and re-averaged structure, it may lose significant information. The MA chart worked better for greater shifts. Furthermore, the MTEM chart competency was applied to two real-world datasets, confirming its practicality.
A Novel Statistical Process Control Approach for PM2.5 Monitoring Using Time Series Modeling Supharakonsakun, Yadpirun; Areepong, Yupaporn
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-05

Abstract

This research seeks to create a novel control chart capable of managing autocorrelated time series data by proposing a modified Exponentially Weighted Moving Average (EWMA) approach tailored to processes following the ARMA(p,q) model, which also makes use of exponential white noise. The key methodological contribution involves an explicit formula to compute the Average Run Length (ARL), while the Numerical Integral Equation (NIE) approach is utilized for verification purposes. The proposed formula not only demonstrated 100% agreement with NIE results but also significantly reduced computational time, requiring less than 0.001 seconds per run, compared to the 3–4 seconds typically needed by NIE. To assess the performance, simulation experiments and real-world case studies on PM2.5 air pollution data from Nakhon Phanom, Nan, and Nonthaburi provinces in Thailand were conducted. Our modified control chart was better at identifying minimal changes than a standard EWMA chart, as shown by lower ARL1, SDRL1, AEQL, and optimal PCI values. The one-sided chart structure, designed to monitor upward shifts in pollutant levels, further supports its application in environmental surveillance. Overall, the study introduces a fast, accurate, and practical tool for quality control in autocorrelated environments, offering both analytical and computational advantages over existing methods.
Evaluating Sensitivity of Double EWMA Chart for ARL Under Trend SAR(1) Model and Applications Areepong, Yupaporn; Karoon, Kotchaporn
Emerging Science Journal Vol. 9 No. 6 (2025): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2025-09-06-026

Abstract

The goal of this study is to offer the precise average run length (ARL) on the Double Exponentially Weighted Moving Average (double EWMA) control chart for the data underlying the first-order seasonal autoregressive (SAR(1)L) with trend model. A comparison was made between the explicit formula and the computed ARL obtained using the numerical integral equation (NIE) approach, employing four quadrature methods: the midpoint, Simpson’s, trapezoidal, and Boole’s rules. The comparison was based on accuracy percentage (%Acc) and computation time (in seconds). The results showed that there was not much variation in accuracy between the ARL results of the explicit ARL and ARL via the NIE method. The findings indicate that the explicit ARL and NIE approaches produce very consistent accuracy values; however, the explicit formula is significantly more rapid (instantaneous compared to 1.5–26 seconds). The advantage of the double EWMA chart compared to the extended EWMA chart in identifying process changes is demonstrated, encompassing evaluations under both one-sided and two-sided setups with varied LCL values. The results are additionally corroborated by sensitivity measures (AEQL, PCI, RMI) and checked with actual durian export data, guaranteeing that the conclusions are firmly established in both simulated and empirical evidence.
Co-Authors Karoon, Kotchaporn Saesuntia, Piyatida Sukparungsee, Saowanit Supharakonsakun, Yadpirun