Control charts based on fuzzy costs for monitoring short autocorrelated time series

Abstract Monitoring the stability of processes described by autocorrelated time series requires dedicated tools such as the Shewhart control chart for residuals. This paper discusses and extends a recently introduced ensemble prediction framework for time series called weighted averaged models (WAM). Central to the WAM approach are the weights of the base and alternative predictive models. We propose eliciting these in a novel way from domain experts using imprecise (fuzzy) costs. We use them to define an X control chart for autocorrelated time series called XWAM. Its application is illustrated by a real-world psychiatric problem, in which the self-assessment data on bipolar disorder patients' moods is used to detect the changes of their mental states.