|
Activity Number:
|
67
- Believable Big Bayes: Large-Scale Bayesian Inference with Finite-Data Guarantees
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
|
|
Sponsor:
|
SSC
|
|
Abstract #306918
|
|
|
Title:
|
Detecting Anomalous Structure in Multivariate Data Streams
|
|
Author(s):
|
Alexander Fisch* and Idris Eckley and Paul Fearnhead
|
|
Companies:
|
Lancaster University and Lancaster University and Lancaster University
|
|
Keywords:
|
Anomaly detection;
Streaming data;
Changepoints;
Multivariate ;
Time series
|
|
Abstract:
|
In recent years there has been a growing interest in identifying anomalous structure within data streams. Much of the work in this area has been focused on the univariate setting, however increasingly the streams that we now observe are more complex with more challenging inference questions to be addressed. This talk will introduce recent work in this area, describing a novel approach for the joint detection of point and collective anomalies within multivariate data streams together with its application to simulated and industrial data.
|
Authors who are presenting talks have a * after their name.
