Classification of extended control chart patterns: a neural networks approach

This paper generalizes the application of artificial neural network (ANN) by classifying six common control chart patterns into eight classes of time series data patterns. Incorporating two more patterns of bottom-out and peak-off can yield better insights for not only the traditional real time control environment but also the behavioral study in other time-domain systems such as money and security markets. This work reports the results of empirical study on the incorporation of new extracted features, especially those with a lesser extent of outliers' effect, for example median, robust regression and RMS value of the time series. The feedforward backprogation ANN is deployed and experimented using two different training schemes, namely the Levenberg-Marquardt method and the Bayesian regularization. The best performance generated by the ANN is 98% classification accuracy. Technical insights into the model settings are also provided.

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