Sofia Olhede (UCL)

Estimation of Nonstationary Time Series

A time series is usually, unless a specific parametric model is assumed, understood from its first and second moments. If the process is also stationary, e.g. its first and second moments are invariant to time translations, then estimation is a mature and well-developed field.

Unfortunately, most observed processes are not stationary, as they are the result of the observation of transient phenomena. Therefore in classical time series analysis a theory has also been developed for the analysis of such processes. There are a number of short-comings of existing and well-developed methods, in particular in how the processes are allowed to evolve in time. I will discuss how to relax existing assumptions and still be able to develop good inference methods from a single time course of observations.