Real-time clustering of noisy time series
Lead Research Organisation:
University of Manchester
Department Name: Mathematics
Abstract
The PhD project is concerned with the development of new approaches for clustering of noisy time series, with an application to real-time measurements being taken in large industrial assets. Firstly, the student will investigate low-dimensional representations of time series which preserve relevant features and allow for automatic classification using techniques from machine learning, such as support vector machines. Secondly, the real-time aspect of data collection will necessitate the need for efficient updating techniques in the linear algebra routines underlying the training and assignment phases.
Organisations
Publications
Elsworth S
(2019)
Conversions between barycentric, RKFUN, and Newton representations of rational interpolants
in Linear Algebra and its Applications
Steven Elsworth
(2020)
The block rational Arnoldi method
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509565/1 | 01/10/2016 | 30/09/2021 | |||
| 1775511 | Studentship | EP/N509565/1 | 01/10/2016 | 31/03/2020 | Steven Elsworth |
| Description | - Introduced a method to convert between different representations of rational interpolant. - Developed the theory behind block rational Krylov spaces. - Established a connection between vector autoregressive time series models and block polynomial Arnoldi decompositions. - Introduced a new symbolic representation of time series. |
| Exploitation Route | Block rational Krylov spaces have many applications including solving eigenvalue problems, matrix equations and model order reduction to name just a few. The new symbolic representation, which we call ABBA, has a variety of applications in the time series data mining community. |
| Sectors | Other |