Speaker
Maha Shadaydeh
Description
The ability to detect and attribute anomalous behavior in multivariate environmental time series is crucial. These events are signals of changes in the underlying dynamical system and understanding them can help to pave the way for the development of intervention strategy. The availability of high temporal resolution data along with the powerful computing platforms further enhance the capacity of data-driven methods in capturing the complex relationships between the variables of the underlying dynamical system. In this talk, we present some newly developed methods for the detection as well as the attribution of changes in multivariate environmental time series. We discuss the challenges and point out future directions.
