[Turing-Southampton] S3RI seminar: Rebecca Killick, Thursday 2-3pm

[Helen Ogden]

Dear all,

On Thursday (18 October) at 2pm in 54 / 7035 (7B), we have an S3RI 
seminar from Rebecca Killick (Lancaster University) on "Computationally 
Efficient Multivariate Changepoint Detection with Subsets". Details are 
given below.

The seminar will also be available via a live web-cast at
https://coursecast.soton.ac.uk/Panopto/Pages/Viewer.aspx?id=ec46c95b-eb59-4da6-b85f-d81b070328c8

The talk will be followed by tea and cake in the staff reading room on 
level 4 of building 54.

All are welcome!

Best wishes,

Helen

Computationally Efficient Multivariate Changepoint Detection with Subsets

Rebecca Killick, Lancaster University

Historically much of the research on changepoint analysis has focused on 
the univariate setting. Due to the growing number of high dimensional 
datasets there is an increasing need for methods that can detect 
changepoints in multivariate time series. In this talk we focus on the 
problem of detecting changepoints where only a subset of the variables 
under observation undergo a change, so called subset multivariate 
changepoints. One approach to locating changepoints is to choose the 
segmentation that minimises a penalised cost function via a dynamic 
program. The work in this presentation is the first to create a dynamic 
program specifically for detecting changes in subset-multivariate time 
series. The computational complexity of the dynamic program means it is 
infeasible even for medium datasets. Thus we propose a computationally 
efficient approximate dynamic program, SPOT. We demonstrate that SPOT 
always recovers a better segmentation, in terms of penalised cost, then 
other approaches which assume every variable changes. Furthermore under 
mild assumptions the computational cost of SPOT is linear in the number 
of data points. In small simulation studies we demonstrate that SPOT 
provides a good approximation to exact methods but is feasible for 
datasets that contain thousands of variables observed at millions of 
time points. Furthermore we demonstrate that our method compares 
favourably with other commonly used multivariate changepoint methods and 
achieves a substantial improvement in performance when compared with 
fully multivariate methods.

For the current schedule of S3RI seminars, see 
https://tinyurl.com/s3riseminar

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