10 citations · 25 across the 9 of their papers we have counts for
9 papers
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…
Online Multivariate Changepoint Detection: Leveraging Links With Computational Geometry
Liudmila Pishchagina, Gaetano Romano, Paul Fearnhead +2
The increasing volume of data streams poses significant computational challenges for detecting changepoints online. Likelihood-based methods are effective, but a naive sequential i…
A Constant-per-Iteration Likelihood Ratio Test for Online Changepoint Detection for Exponential Family Models
Kes Ward, Gaetano Romano, Idris Eckley +1
Online changepoint detection algorithms that are based on likelihood-ratio tests have been shown to have excellent statistical properties. However, a simple online implementation i…
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…
gfpop: an R Package for Univariate Graph-Constrained Change-Point Detection
Vincent Runge, Toby Dylan Hocking, Gaetano Romano +3
In a world with data that change rapidly and abruptly, it is important to detect those changes accurately. In this paper we describe an R package implementing a generalized version…