Monitoring of count data time series: Cumulative sum change detection in Poisson integer valued GARCH models. Issue Volume 31:Issues 3(2019) (3rd July 2019)

• Record Type: Journal Article
• Authors: Vanli, O. Arda; Giroux, Rupert; Erman Ozguven, Eren; Pignatiello, Joseph J.
• Is Part Of: Quality engineering. Volume 31:Issues 3(2019)
• Page Start: 439
• Page End: 452
• Publication Date: 2019-07-03
• Subjects: Prospective monitoring -- cumulative sum -- count data time series -- crash data; Quality control -- Periodicals; Production management -- Quality control -- Periodicals; 658.5
• DOI: 10.1080/08982112.2018.1508696
• Languages: English
• ISSNs: 0898-2112

Abstract

Abstract: This article presents a cumulative sum (CUSUM) monitoring approach for count-data time series. A seasonal integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH(1, 1)) time series model with Poisson deviates is used to develop a likelihood ratio test formulation to detect changes in the process accounting for temporal correlations and seasonality. Simulation studies show that the proposed CUSUM monitoring approach can provide significantly improved performance in applications where serial correlation or seasonality is prevalent. A case study with real traffic crash counts is presented to illustrate the application of the proposed methodology for roadway safety improvement.