Smooth and abrupt changes in autoregressive tensor models

Thomas M. Menino Convention & Exhibition Center 

In this talk, we propose an autoregressive tensor model, with time-dependent regression coefficients. With respect to the regression coefficients, three different regimes are considered: stationary, smooth-varying and abruptly-changing. We propose a computationally-efficient estimation procedure to handle these three regimes simultaneously, supported with theoretical guarantees and numerical experiments. Two extensions are considered: dynamic community detection in these three regimes and an estimation procedure for a more general class of time series models.