Detecting pattern transitions in psychological time series - A validation study on the Pattern Transition Detection Algorithm (PTDA).

ABSTRACT: With the increasing use of real-time monitoring procedures in clinical practice, psychological time series become available to researchers and practitioners. An important interest concerns the identification of pattern transitions which are characteristic features of psychotherapeutic change. Change Point Analysis (CPA) is an established method to identify the point where the mean and/or variance of a time series change, but changes of other and more complex features cannot be detected by this method. In this study, an extension of the CPA, the Pattern Transition Detection Algorithm (PTDA), is optimized and validated for psychological time series with complex pattern transitions. The algorithm uses the convergent information of the CPA and other methods like Recurrence Plots, Time Frequenc

SUBMITTER: Viol K 

PROVIDER: S-EPMC8916631 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature