STATISTICAL MODELING FOR COMPLEX FUNCTIONAL AND NETWORK TIME SERIES DATA

Functional and network time series data have been available, which provide richer information that brings opportunities and potentials to solve scientific problems. On the other hand, the complex structure presents challenges to conventional statistical analytical tools in terms of estimation and prediction. We developed three statistical modeling to investigate the dynamic behaviors of high dimensional data with either seasonality entangling with serial dependence or cross dependence among multiple time series data or network connection. In particular, we developed a Warping Functional AutoRegressive (WFAR) model that simultaneously accounts for the cross time-dependence and seasonal variations of the curves. Moreover, we proposed common functional principal component (CFPC) based autoregressive model, where common factors of a series of curves are identified using the CFPC method. Last, we proposed a Sparse Group Network AutoRegressive (SGNAR) model to describe the dynamic dependence structure of network. All the proposed models were driven by real data, where the implementation results illustrated promising performance.

Author dc:creator

• ZHANG JIEJIE

• Seasonal functional time series, network time series, time warping, CFPCA, lasso, group lasso