A Wavelet-Based Nonparametric CUSUM Control Chart for Autocorrelated Processes with Applications to Network Surveillance

• Activity Number: 391 - Statistical Process Control for Complex Data Structures
• Type: Topic Contributed
• Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Quality and Productivity Section
• Author(s): Daniel Jeske* and Jun Li and Yangmei Zhou
• Companies: University of California, Riverside and University of California, Riverside and University of California, Riverside
• Keywords: Wavelets ; SPC ; CUSUM

Abstract

Statistical Process Control (SPC) has natural applications in network surveillance. However, network data are commonly autocorrelated which presents challenges to the basic SPC methods. Most existing SPC methods for correlated data assume parametric models to account for the correlation structure within the data. Those model assumptions can be difficult to justify in practice. In this paper, we propose a nonparametric cumulative sum (CUSUM) control chart for autocorrelated processes. In our proposed approach, we incorporate a wavelet decomposition and a nonparametric multivariate CUSUM control chart to obtain a robust procedure for autocorrelated processes without distribution assumptions. Extensive simulations show that the procedure appropriately controls the in-control average run length and also has excellent sensitivity for detecting location shifts. We apply the proposed procedure to real network data to demonstrate its performance.