10 citations · 25 across the 9 of their papers we have counts for
6 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…
Exact Multiple Change-Point Detection Via Smallest Valid Partitioning
Vincent Runge, Anica Kostic, Alexandre Combeau +1
We introduce smallest valid partitioning (SVP), a segmentation method for multiple change-point detection in time-series. SVP relies on a local notion of segment validity: a candid…
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…
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…
Detecting Abrupt Changes in the Presence of Local Fluctuations and Autocorrelated Noise
Gaetano Romano, Guillem Rigaill, Vincent Runge +1
Whilst there are a plethora of algorithms for detecting changes in mean in univariate time-series, almost all struggle in real applications where there is autocorrelated noise or w…