Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis.

ABSTRACT: We are concerned with the issue of detecting changes and their signs from a data stream. For example, when given time series of COVID-19 cases in a region, we may raise early warning signals of an epidemic by detecting signs of changes in the data. We propose a novel methodology to address this issue. The key idea is to employ a new information-theoretic notion, which we call the differential minimum description length change statistics (D-MDL), for measuring the scores of change sign. We first give a fundamental theory for D-MDL. We then demonstrate its effectiveness using synthetic datasets. We apply it to detecting early warning signals of the COVID-19 epidemic using time series of the cases for individual countries. We empirically demonstrate that D-MDL is able to raise early warning s

SUBMITTER: Yamanishi K 

PROVIDER: S-EPMC8492813 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature