A consistent data model for different data granularity in control charts. Issue Volume 34:Issues 2(2022) (3rd April 2022)

• Record Type: Journal Article
• Authors: Grimshaw, Scott D.
• Is Part Of: Quality engineering. Volume 34:Issues 2(2022)
• Page Start: 290
• Page End: 303
• Publication Date: 2022-04-03
• Subjects: Autocorrelation -- sampling frequency -- aggregation -- time series analysis -- timestamp; Quality control -- Periodicals; Production management -- Quality control -- Periodicals; 658.5
• DOI: 10.1080/08982112.2022.2042700
• Languages: English
• ISSNs: 0898-2112

Abstract

Abstract: After a long-running show was canceled, control charts are used to identify if and when viewing drops. The finest granularity daily viewing has high autocorrelation and control charts use residuals from a seasonal ARIMA model. For coarse granularity data (weekly and monthly viewing) an approximate AR model is derived to be consistent with the finest granularity model. With the proposed approach, a longer memory model is used in the granular data control charts that reduces the number of false alarms from control charts constructed treating granular data as a different measurement.