69 citations · 152 across the 20 of their papers we have counts for
8 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…
Geometric-Based Pruning Rules For Change Point Detection in Multiple Independent Time Series
Liudmila Pishchagina, Guillem Rigaill, Vincent Runge
We consider the problem of detecting multiple changes in multiple independent time series. The search for the best segmentation can be expressed as a minimization problem over a gi…
Ms.FPOP: An Exact and Fast Segmentation Algorithm With a Multiscale Penalty
Arnaud Liehrmann, Guillem Rigaill
Given a time series in with a piecewise constant mean and independent noises, we propose an exact dynamic programming algorithm to minimize a least square criterion with a mu…
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…
Adjusting the adjusted Rand Index -- A multinomial story
Martina Sundqvist, Julien Chiquet, Guillem Rigaill
The Adjusted Rand Index () is arguably one of the most popular measures for cluster comparison. The adjustment of the is based on a hypergeometric distribution assumptio…
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…
Changepoint Detection in the Presence of Outliers
Paul Fearnhead, Guillem Rigaill
Many traditional methods for identifying changepoints can struggle in the presence of outliers, or when the noise is heavy-tailed. Often they will infer additional changepoints in…
On Optimal Multiple Changepoint Algorithms for Large Data
Robert Maidstone, Toby Hocking, Guillem Rigaill +1
There is an increasing need for algorithms that can accurately detect changepoints in long time-series, or equivalent, data. Many common approaches to detecting changepoints, for e…