2.7k citations · 2.8k across the 32 of their papers we have counts for
16 papers · 1 filter
focus and focus-cpt: Fast Online Changepoint Detection in R and Python
Gaetano Romano, Kes Ward, Yuntang Fan +4
We present an R and Python package for fast online changepoint detection in univariate and multivariate data streams for a variety of models. The package implements the focus famil…
An Efficient Likelihood Ratio Test for Online Changepoint Detection in the Presence of Autocorrelation
Yuntang Fan, Paul Fearnhead, Idris A. Eckley +1
Changepoint detection methods have seen considerable development in recent years, with online algorithms capable of identifying structural changes in streaming data in near real ti…
Detection of collective and point anomalies in the presence of trend and seasonality
Yiyin Zhang, Florian Pein, Idris A. Eckley
Detecting anomalies in time series data is a challenging task with broad relevance in many applications. Existing methods work effectively only under idealized conditions, typicall…
A communication-efficient, online changepoint detection method for monitoring distributed sensor networks
Ziyang Yang, Idris A. Eckley, Paul Fearnhead
We consider the challenge of efficiently detecting changes within a network of sensors, where we also need to minimise communication between sensors and the cloud. We propose an on…
Automatic Locally Stationary Time Series Forecasting with application to predicting U.K. Gross Value Added Time Series under sudden shocks caused by the COVID pandemic
Rebecca Killick, Marina I. Knight, Guy P. Nason +2
Accurate forecasting of the U.K. gross value added (GVA) is fundamental for measuring the growth of the U.K. economy. A common nonstationarity in GVA data, such as the ABML series,…
A Log-Linear Non-Parametric Online Changepoint Detection Algorithm based on Functional Pruning
Gaetano Romano, Idris A Eckley, Paul Fearnhead
Online changepoint detection aims to detect anomalies and changes in real-time in high-frequency data streams, sometimes with limited available computational resources. This is an…
High-dimensional time series segmentation via factor-adjusted vector autoregressive modelling
Haeran Cho, Hyeyoung Maeng, Idris A. Eckley +1
Vector autoregressive (VAR) models are popularly adopted for modelling high-dimensional time series, and their piecewise extensions allow for structural changes in the data. In VAR…
Fast Online Changepoint Detection via Functional Pruning CUSUM statistics
Gaetano Romano, Idris Eckley, Paul Fearnhead +1
Many modern applications of online changepoint detection require the ability to process high-frequency observations, sometimes with limited available computational resources. Onlin…
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…
Parallelisation of a Common Changepoint Detection Method
S. O. Tickle, I. A. Eckley, P. Fearnhead +1
In recent years, various means of efficiently detecting changepoints in the univariate setting have been proposed, with one popular approach involving minimising a penalised cost f…
Optimal detection of changepoints with a linear computational cost
R. Killick, P. Fearnhead, I. A. Eckley
We consider the problem of detecting multiple changepoints in large data sets. Our focus is on applications where the number of changepoints will increase as we collect more data:…