Recent Advances and Trends in Time Series Analysis: Nonlinear Time Series, High Dimensional Inference and Beyond (14w5157)
Yulia Gel (University of Waterloo)
Rafal Kulik (University of Ottawa)
Hao Yu (University of Western Ontario)
It is now well recognized that theoretical and methodological advances in time series analysis are possible only if the gaps between paradigms of various branches of statistics and applied sciences are bridged together, forging joint interdisciplinary efforts. The proposed workshop aims to take advantage from the state-of-the-art research from multiple disciplines and builds on the fusion of complementary expertise of leading researchers in time series, nonparametrics, multivariate analysis, functional data analysis, applied probability and computational statistics.
The unifying theme for the conference will be start-of-the-art methodology and applications of time series, in a general sense. Remarkably, while the importance of time series methodology is well recognized in statistics and applied fields, there has been recently no major events on time series analysis not only in Canada but even in North America. Hence, the workshop aims to fill this gap and to enhance exchange of research ideas on new challenges for the theoretical foundations as well as the implementation and numerical properties of modern time series. In particular, the conference will explore the current state and future directions needed for such very important class of data problems as nonlinear time series and high dimensional inference for time series.