Monitoring serially dependent processes using conventional control charts yields a high false alarm rate. Multioutput Least Squares Support Vector Regression (MLS-SVR) has the capability to encompass the cross-relatedness between output variables by learning multivariate output variables simultaneously. This research aims to develop a Multivariate Cumulative Sum (MCUSUM) control chart based on the residual obtained from the MLS-SVR model for monitoring autocorrelated data. The inputs of the MLS-SVR are selected using the significant lag of a partial autocorrelation function. The proposed control chart is applied to monitor water quality data and it can detect the assignable causes in those data caused by a broken pipeline.

Original languageEnglish
Pages (from-to)73-83
Number of pages11
JournalMalaysian Journal of Science
Publication statusPublished - 2019


  • Autocorrelated
  • Control chart
  • Multioutput least squares SVR
  • Multivariate CUSUM
  • Water quality


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