3 citations · 5 across the 5 of their papers we have counts for
7 papers · 1 filter
Collective anomaly detection in High-dimensional VAR Models
Hyeyoung Maeng, Idris Eckley, Paul Fearnhead
There is increasing interest in detecting collective anomalies: potentially short periods of time where the features of data change before reverting back to normal behaviour. We pr…
Scalable changepoint and anomaly detection in cross-correlated data with an application to condition monitoring
Martin Tveten, Idris A. Eckley, Paul Fearnhead
Motivated by a condition monitoring application arising from subsea engineering we derive a novel, scalable approach to detecting anomalous mean structure in a subset of correlated…
Real Time Anomaly Detection And Categorisation
Alexander T. M. Fisch, Lawrence Bardwell, Idris A. Eckley
The ability to quickly and accurately detect anomalous structure within data sequences is an inference challenge of growing importance. This work extends recently proposed post-hoc…
Innovative And Additive Outlier Robust Kalman Filtering With A Robust Particle Filter
Alexander T. M. Fisch, Idris A. Eckley, P. Fearnhead
In this paper, we propose CE-BASS, a particle mixture Kalman filter which is robust to both innovative and additive outliers, and able to fully capture multi-modality in the distri…
A novel change point approach for the detection of gas emission sources using remotely contained concentration data
Idris Eckley, Claudia Kirch, Silke Weber
Motivated by an example from remote sensing of gas emission sources, we derive two novel change point procedures for multivariate time series where, in contrast to classical change…
Subset Multivariate Collective And Point Anomaly Detection
Alexander T M Fisch, Idris A Eckley, Paul Fearnhead
In recent years, there has been a growing interest in identifying anomalous structure within multivariate data streams. We consider the problem of detecting collective anomalies, c…