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
Steven Elsworth
(2020)
The block rational Arnoldi method
Elsworth S
(2019)
Conversions between barycentric, RKFUN, and Newton representations of rational interpolants
in Linear Algebra and its Applications
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 |