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Detection and Exploitation of Nonstationarities in Time Series Data

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If you have a question about this talk, please contact Mustapha Amrani.

Inference for Change-Point and Related Processes

In recent years, with disciplines becoming increasingly quantitative, time series exhibiting nonstationary characteristics have proliferated. Such proliferation is partly due to our improved ability to collect longer series enabling the detection of nonstationarities and nonlinearities that could not be found before. Time series are more complex too, not only are we faced by classical univariate and multivariate series but time series on graphs, manifolds and fractal-like domains. This talk provides an overview on the detection of nonstationarities and how to exploit them to provide a richer understanding of their underlying characteristics. We exhibit these techniques on a variety of data sets arising in earth sciences, economics and epidemics.

This talk is part of the Isaac Newton Institute Seminar Series series.

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