Robust Control Chart for Time Series Data

Control charts are the most useful tools for controlling the processes statistically. The construction of the control charts requires the estimation of the process parameters using random sample data. Usually the classical estimators of the process parameters are used to construct the control charts. The classical estimators of the parameters of the processes generating autocorrelated data are sensitive to the presence of the outlier observations. Applying classical methods of estimation while outliers are present, introduce biased estimates of the model parameters which result in wrong interpretation of the control chart. In this research a method called Iteratively Robust Filtered Fast Tau (IRFFT) which is insensitive to the presence of the outliers is proposed for estimating the parameters of the autocorrelated models. The newly introduced estimators are used to construct robust control chart for autocorrelated data. The suggested robust control chart is compared with the control chart whose parameters are estimated using LS method. Results of the simulation study for the two methods indicate that the ARL for the suggested robust control chart is much smaller under different scenarios. The findings may be extended to the other time series models.