Université de Toronto
6 octobre 2023 de 15 h 30 à 16 h 30 (heure de Montréal/HNE) Sur place
Understanding the time-varying structure of complex temporal systems is one of the main challenges of modern time series analysis. In this talk, I will demonstrate that a wide range of short-range dependent non-stationary and nonlinear time series can be well approximated globally by a white-noise-driven auto-regressive (AR) process of slowly diverging order. Uniform statistical inference of the latter AR structure will be discussed through a class of high-dimensional L2 tests. I will further discuss applications of the AR approximation theory to globally optimal short-term forecasting, efficient estimation, and resampling inference under complex temporal dynamics.
AdresseCentre de recherches mathématiques Pavillon André-Aisenstadt, Université de Montréal salle 6214