New Approaches to Statistical Learning of Modern Time Series

Abstract

The BIGDATA era, many new forms of data have become available and useful in various important applications. When these data are observed over time, they form new types of time series that require new statistical models and analytical tools in order to extract useful information. In this talk we present new developments in analyzing matrix and tensor time series, dynamic networks, functional time series and compositional time series, with applications ranging from economics, finance, international trade, electricity loading and others. We will also briefly discuss approaches on modeling other forms of time series, including text time series and dynamic social network.