How can we spot a change in a jet engine vibration that might mean it’s about to fail catastrophically? How can a service forecast adapt to unexpected changes brought about by a pandemic? How might we spot an increase in rate of change of pollution in the atmosphere? The answer to all these questions is changepoints, or rather changepoint detection.
Common to all these systems is a set of ordered data, usually a time series of observations or measurements that may be noisy but have some underlying pattern. As the world changes, so those changes might lead to dramatic changes in the measurements and a disruption of the usual pattern. Unless these forecasts or failure-detection systems are updated quickly to take account of a change in measurement data, they will likely produce erroneous or unpredictable results.
Changepoints have many important applications in areas such as:
We speak to statistician Dr. Rebecca Killick from Lancaster University about her work in changepoint detection and how it is a critical part of the statistical toolkit for analysing time series and other ordered data sets. In particular:
With interview guest Dr. Rebecca Killick, Associate Professor of Statistics at Lancaster University.
Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.
Recording date: 10 June 2020
Interview date: 5 June 2020
Thanks for joining us in the DataCafé. You can follow us on twitter @DataCafePodcast and feel free to contact us about anything you've heard here or think would be an interesting topic in the future.