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Quebec Mathematics Sciences Colloquium

Title: Auto-regressive approximations to non-stationary time series, with inference and applications (2023 CRM-SSC Prize Lecture)

Abstract
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.

the CRM, Room 6214, Pavillon Andr茅-Aisenstadt

Universit茅 de Montr茅al

The presentation will also be accessible using Zoom with the following information:

• Meeting ID: 842 2670 1306,
• Passcode: 692788.