Time Series Analysis and Statistical Process Control for Anomaly Detection: An Application to the Situation of PM 10 in Northern Thailand

Unchalee Tonggumnead; Kittipong Klinjan

doi:10.4028/www.scientific.net/AMM.866.379

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 866Time Series Analysis and Statistical Process...

Time Series Analysis and Statistical Process Control for Anomaly Detection: An Application to the Situation of PM 10 in Northern Thailand

Abstract:

The monitoring of processes is a vital mechanism for ensuring that such processes remain safe and under control. The present research aims to solve problems associated with correlated data by applying the Box-Jenkins method integrated with statistical process control (SPC) tools, namely the Shewhart chart, the moving average chart, the cumulative sum (CUSUM) chart, and the exponentially weighted moving-average (EWMA) chart. The efficiency of the four SPC tools was also compared in terms of the false alarm rate (FAR) and the missed detection rate (MDR). The findings indicated that the EWMA chart was the most effective in detecting anomaly, the Shewhart chart and the moving average chart produced high MDR, and the CUSUM chart suffered the highest FAR.

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Periodical:

Applied Mechanics and Materials (Volume 866)

Pages:

379-382

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.866.379

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Online since:

June 2017

Authors:

Unchalee Tonggumnead*, Kittipong Klinjan

Keywords:

Box-Jenkins Method, Control Chart, Statistical Process Control, Time Series Analysis

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