Control chart based on residues: Is a good methodology to detect outliers?

Abstract The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individua...
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Gespeichert in:

Autor*in:	Guarnieri, Jean Paulo [verfasserIn] Souza, Adriano Mendonça [verfasserIn] Jacobi, Luciane Flores [verfasserIn] Reichert, Bianca [verfasserIn] da Veiga, Claudimar Pereira [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Quality control Residual control charts Outliers Efficiency of control charts

Übergeordnetes Werk:	Enthalten in: Journal of industrial engineering international - Heidelberg : SpringerOpen, 2005, 15(2019), Suppl 1 vom: 27. Juli, Seite 119-130
Übergeordnetes Werk:	volume:15 ; year:2019 ; number:Suppl 1 ; day:27 ; month:07 ; pages:119-130

Links:	Volltext

DOI / URN:	10.1007/s40092-019-00324-0

Katalog-ID:	SPR032828519

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520			\|a Abstract The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to residuals of models of AR(1) or MA(1) to detect outlier in autocorrelated processes. Considering autocorrelation strength and sign in the data series and the outlier range, the series were simulated accomplishing 640,000 sets. The series were contaminated by anomalous observations at 100th position, an AR(1) or MA(1) model were fitted, and the residuals were evaluated by CCIO and EWMA control charts; the points correctly detected as an autocorrelation were recorded. For the parameters investigated (autocorrelation and outlier range), a detection rate was generated in each chart, and nonparametric comparison tests were applied. The result of the tests showed the superiority (p < 0.05) of the CCIO chart for both models. The study of the influence of the sign and magnitude of the autocorrelation parameter showed no significant (p > 0.05) for either AR(1) or MA(1) charts and models. In this context, control charts for individual observations (CCIO) were confirmed to effectively detect outliers through residuals in industrial autocorrelated processes originated in first-order AR and MA models.
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allfields	10.1007/s40092-019-00324-0 doi (DE-627)SPR032828519 (SPR)s40092-019-00324-0-e DE-627 ger DE-627 rakwb eng 620 ASE Guarnieri, Jean Paulo verfasserin aut Control chart based on residues: Is a good methodology to detect outliers? 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to residuals of models of AR(1) or MA(1) to detect outlier in autocorrelated processes. Considering autocorrelation strength and sign in the data series and the outlier range, the series were simulated accomplishing 640,000 sets. The series were contaminated by anomalous observations at 100th position, an AR(1) or MA(1) model were fitted, and the residuals were evaluated by CCIO and EWMA control charts; the points correctly detected as an autocorrelation were recorded. For the parameters investigated (autocorrelation and outlier range), a detection rate was generated in each chart, and nonparametric comparison tests were applied. The result of the tests showed the superiority (p < 0.05) of the CCIO chart for both models. The study of the influence of the sign and magnitude of the autocorrelation parameter showed no significant (p > 0.05) for either AR(1) or MA(1) charts and models. In this context, control charts for individual observations (CCIO) were confirmed to effectively detect outliers through residuals in industrial autocorrelated processes originated in first-order AR and MA models. Quality control (dpeaa)DE-He213 Residual control charts (dpeaa)DE-He213 Outliers (dpeaa)DE-He213 Efficiency of control charts (dpeaa)DE-He213 Residual control charts (dpeaa)DE-He213 Souza, Adriano Mendonça verfasserin aut Jacobi, Luciane Flores verfasserin aut Reichert, Bianca verfasserin aut da Veiga, Claudimar Pereira verfasserin aut Enthalten in Journal of industrial engineering international Heidelberg : SpringerOpen, 2005 15(2019), Suppl 1 vom: 27. Juli, Seite 119-130 (DE-627)716633795 (DE-600)2664907-X 2251-712X nnns volume:15 year:2019 number:Suppl 1 day:27 month:07 pages:119-130 https://dx.doi.org/10.1007/s40092-019-00324-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2019 Suppl 1 27 07 119-130
spelling	10.1007/s40092-019-00324-0 doi (DE-627)SPR032828519 (SPR)s40092-019-00324-0-e DE-627 ger DE-627 rakwb eng 620 ASE Guarnieri, Jean Paulo verfasserin aut Control chart based on residues: Is a good methodology to detect outliers? 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to residuals of models of AR(1) or MA(1) to detect outlier in autocorrelated processes. Considering autocorrelation strength and sign in the data series and the outlier range, the series were simulated accomplishing 640,000 sets. The series were contaminated by anomalous observations at 100th position, an AR(1) or MA(1) model were fitted, and the residuals were evaluated by CCIO and EWMA control charts; the points correctly detected as an autocorrelation were recorded. For the parameters investigated (autocorrelation and outlier range), a detection rate was generated in each chart, and nonparametric comparison tests were applied. The result of the tests showed the superiority (p < 0.05) of the CCIO chart for both models. The study of the influence of the sign and magnitude of the autocorrelation parameter showed no significant (p > 0.05) for either AR(1) or MA(1) charts and models. In this context, control charts for individual observations (CCIO) were confirmed to effectively detect outliers through residuals in industrial autocorrelated processes originated in first-order AR and MA models. Quality control (dpeaa)DE-He213 Residual control charts (dpeaa)DE-He213 Outliers (dpeaa)DE-He213 Efficiency of control charts (dpeaa)DE-He213 Residual control charts (dpeaa)DE-He213 Souza, Adriano Mendonça verfasserin aut Jacobi, Luciane Flores verfasserin aut Reichert, Bianca verfasserin aut da Veiga, Claudimar Pereira verfasserin aut Enthalten in Journal of industrial engineering international Heidelberg : SpringerOpen, 2005 15(2019), Suppl 1 vom: 27. Juli, Seite 119-130 (DE-627)716633795 (DE-600)2664907-X 2251-712X nnns volume:15 year:2019 number:Suppl 1 day:27 month:07 pages:119-130 https://dx.doi.org/10.1007/s40092-019-00324-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2019 Suppl 1 27 07 119-130
allfields_unstemmed	10.1007/s40092-019-00324-0 doi (DE-627)SPR032828519 (SPR)s40092-019-00324-0-e DE-627 ger DE-627 rakwb eng 620 ASE Guarnieri, Jean Paulo verfasserin aut Control chart based on residues: Is a good methodology to detect outliers? 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to residuals of models of AR(1) or MA(1) to detect outlier in autocorrelated processes. Considering autocorrelation strength and sign in the data series and the outlier range, the series were simulated accomplishing 640,000 sets. The series were contaminated by anomalous observations at 100th position, an AR(1) or MA(1) model were fitted, and the residuals were evaluated by CCIO and EWMA control charts; the points correctly detected as an autocorrelation were recorded. For the parameters investigated (autocorrelation and outlier range), a detection rate was generated in each chart, and nonparametric comparison tests were applied. The result of the tests showed the superiority (p < 0.05) of the CCIO chart for both models. The study of the influence of the sign and magnitude of the autocorrelation parameter showed no significant (p > 0.05) for either AR(1) or MA(1) charts and models. In this context, control charts for individual observations (CCIO) were confirmed to effectively detect outliers through residuals in industrial autocorrelated processes originated in first-order AR and MA models. Quality control (dpeaa)DE-He213 Residual control charts (dpeaa)DE-He213 Outliers (dpeaa)DE-He213 Efficiency of control charts (dpeaa)DE-He213 Residual control charts (dpeaa)DE-He213 Souza, Adriano Mendonça verfasserin aut Jacobi, Luciane Flores verfasserin aut Reichert, Bianca verfasserin aut da Veiga, Claudimar Pereira verfasserin aut Enthalten in Journal of industrial engineering international Heidelberg : SpringerOpen, 2005 15(2019), Suppl 1 vom: 27. Juli, Seite 119-130 (DE-627)716633795 (DE-600)2664907-X 2251-712X nnns volume:15 year:2019 number:Suppl 1 day:27 month:07 pages:119-130 https://dx.doi.org/10.1007/s40092-019-00324-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2019 Suppl 1 27 07 119-130
allfieldsGer	10.1007/s40092-019-00324-0 doi (DE-627)SPR032828519 (SPR)s40092-019-00324-0-e DE-627 ger DE-627 rakwb eng 620 ASE Guarnieri, Jean Paulo verfasserin aut Control chart based on residues: Is a good methodology to detect outliers? 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to residuals of models of AR(1) or MA(1) to detect outlier in autocorrelated processes. Considering autocorrelation strength and sign in the data series and the outlier range, the series were simulated accomplishing 640,000 sets. The series were contaminated by anomalous observations at 100th position, an AR(1) or MA(1) model were fitted, and the residuals were evaluated by CCIO and EWMA control charts; the points correctly detected as an autocorrelation were recorded. For the parameters investigated (autocorrelation and outlier range), a detection rate was generated in each chart, and nonparametric comparison tests were applied. The result of the tests showed the superiority (p < 0.05) of the CCIO chart for both models. The study of the influence of the sign and magnitude of the autocorrelation parameter showed no significant (p > 0.05) for either AR(1) or MA(1) charts and models. In this context, control charts for individual observations (CCIO) were confirmed to effectively detect outliers through residuals in industrial autocorrelated processes originated in first-order AR and MA models. Quality control (dpeaa)DE-He213 Residual control charts (dpeaa)DE-He213 Outliers (dpeaa)DE-He213 Efficiency of control charts (dpeaa)DE-He213 Residual control charts (dpeaa)DE-He213 Souza, Adriano Mendonça verfasserin aut Jacobi, Luciane Flores verfasserin aut Reichert, Bianca verfasserin aut da Veiga, Claudimar Pereira verfasserin aut Enthalten in Journal of industrial engineering international Heidelberg : SpringerOpen, 2005 15(2019), Suppl 1 vom: 27. Juli, Seite 119-130 (DE-627)716633795 (DE-600)2664907-X 2251-712X nnns volume:15 year:2019 number:Suppl 1 day:27 month:07 pages:119-130 https://dx.doi.org/10.1007/s40092-019-00324-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2019 Suppl 1 27 07 119-130
allfieldsSound	10.1007/s40092-019-00324-0 doi (DE-627)SPR032828519 (SPR)s40092-019-00324-0-e DE-627 ger DE-627 rakwb eng 620 ASE Guarnieri, Jean Paulo verfasserin aut Control chart based on residues: Is a good methodology to detect outliers? 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to residuals of models of AR(1) or MA(1) to detect outlier in autocorrelated processes. Considering autocorrelation strength and sign in the data series and the outlier range, the series were simulated accomplishing 640,000 sets. The series were contaminated by anomalous observations at 100th position, an AR(1) or MA(1) model were fitted, and the residuals were evaluated by CCIO and EWMA control charts; the points correctly detected as an autocorrelation were recorded. For the parameters investigated (autocorrelation and outlier range), a detection rate was generated in each chart, and nonparametric comparison tests were applied. The result of the tests showed the superiority (p < 0.05) of the CCIO chart for both models. The study of the influence of the sign and magnitude of the autocorrelation parameter showed no significant (p > 0.05) for either AR(1) or MA(1) charts and models. In this context, control charts for individual observations (CCIO) were confirmed to effectively detect outliers through residuals in industrial autocorrelated processes originated in first-order AR and MA models. Quality control (dpeaa)DE-He213 Residual control charts (dpeaa)DE-He213 Outliers (dpeaa)DE-He213 Efficiency of control charts (dpeaa)DE-He213 Residual control charts (dpeaa)DE-He213 Souza, Adriano Mendonça verfasserin aut Jacobi, Luciane Flores verfasserin aut Reichert, Bianca verfasserin aut da Veiga, Claudimar Pereira verfasserin aut Enthalten in Journal of industrial engineering international Heidelberg : SpringerOpen, 2005 15(2019), Suppl 1 vom: 27. Juli, Seite 119-130 (DE-627)716633795 (DE-600)2664907-X 2251-712X nnns volume:15 year:2019 number:Suppl 1 day:27 month:07 pages:119-130 https://dx.doi.org/10.1007/s40092-019-00324-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2019 Suppl 1 27 07 119-130
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source	Enthalten in Journal of industrial engineering international 15(2019), Suppl 1 vom: 27. Juli, Seite 119-130 volume:15 year:2019 number:Suppl 1 day:27 month:07 pages:119-130
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author	Guarnieri, Jean Paulo
spellingShingle	Guarnieri, Jean Paulo ddc 620 misc Quality control misc Residual control charts misc Outliers misc Efficiency of control charts Control chart based on residues: Is a good methodology to detect outliers?
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topic_title	620 ASE Control chart based on residues: Is a good methodology to detect outliers? Quality control (dpeaa)DE-He213 Residual control charts (dpeaa)DE-He213 Outliers (dpeaa)DE-He213 Efficiency of control charts (dpeaa)DE-He213
topic	ddc 620 misc Quality control misc Residual control charts misc Outliers misc Efficiency of control charts
topic_unstemmed	ddc 620 misc Quality control misc Residual control charts misc Outliers misc Efficiency of control charts
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title	Control chart based on residues: Is a good methodology to detect outliers?
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title_full	Control chart based on residues: Is a good methodology to detect outliers?
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author_browse	Guarnieri, Jean Paulo Souza, Adriano Mendonça Jacobi, Luciane Flores Reichert, Bianca da Veiga, Claudimar Pereira
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title_sort	control chart based on residues: is a good methodology to detect outliers?
title_auth	Control chart based on residues: Is a good methodology to detect outliers?
abstract	Abstract The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to residuals of models of AR(1) or MA(1) to detect outlier in autocorrelated processes. Considering autocorrelation strength and sign in the data series and the outlier range, the series were simulated accomplishing 640,000 sets. The series were contaminated by anomalous observations at 100th position, an AR(1) or MA(1) model were fitted, and the residuals were evaluated by CCIO and EWMA control charts; the points correctly detected as an autocorrelation were recorded. For the parameters investigated (autocorrelation and outlier range), a detection rate was generated in each chart, and nonparametric comparison tests were applied. The result of the tests showed the superiority (p < 0.05) of the CCIO chart for both models. The study of the influence of the sign and magnitude of the autocorrelation parameter showed no significant (p > 0.05) for either AR(1) or MA(1) charts and models. In this context, control charts for individual observations (CCIO) were confirmed to effectively detect outliers through residuals in industrial autocorrelated processes originated in first-order AR and MA models.
abstractGer	Abstract The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to residuals of models of AR(1) or MA(1) to detect outlier in autocorrelated processes. Considering autocorrelation strength and sign in the data series and the outlier range, the series were simulated accomplishing 640,000 sets. The series were contaminated by anomalous observations at 100th position, an AR(1) or MA(1) model were fitted, and the residuals were evaluated by CCIO and EWMA control charts; the points correctly detected as an autocorrelation were recorded. For the parameters investigated (autocorrelation and outlier range), a detection rate was generated in each chart, and nonparametric comparison tests were applied. The result of the tests showed the superiority (p < 0.05) of the CCIO chart for both models. The study of the influence of the sign and magnitude of the autocorrelation parameter showed no significant (p > 0.05) for either AR(1) or MA(1) charts and models. In this context, control charts for individual observations (CCIO) were confirmed to effectively detect outliers through residuals in industrial autocorrelated processes originated in first-order AR and MA models.
abstract_unstemmed	Abstract The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to residuals of models of AR(1) or MA(1) to detect outlier in autocorrelated processes. Considering autocorrelation strength and sign in the data series and the outlier range, the series were simulated accomplishing 640,000 sets. The series were contaminated by anomalous observations at 100th position, an AR(1) or MA(1) model were fitted, and the residuals were evaluated by CCIO and EWMA control charts; the points correctly detected as an autocorrelation were recorded. For the parameters investigated (autocorrelation and outlier range), a detection rate was generated in each chart, and nonparametric comparison tests were applied. The result of the tests showed the superiority (p < 0.05) of the CCIO chart for both models. The study of the influence of the sign and magnitude of the autocorrelation parameter showed no significant (p > 0.05) for either AR(1) or MA(1) charts and models. In this context, control charts for individual observations (CCIO) were confirmed to effectively detect outliers through residuals in industrial autocorrelated processes originated in first-order AR and MA models.
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title_short	Control chart based on residues: Is a good methodology to detect outliers?
url	https://dx.doi.org/10.1007/s40092-019-00324-0
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author2	Souza, Adriano Mendonça Jacobi, Luciane Flores Reichert, Bianca da Veiga, Claudimar Pereira
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up_date	2024-07-03T14:59:49.998Z
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The study of the influence of the sign and magnitude of the autocorrelation parameter showed no significant (p > 0.05) for either AR(1) or MA(1) charts and models. 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