activity
20192025
most citedHigh-Dimensional Changepoint Detection via a Geometrically Inspired Mapping

31 citations · 41 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ME2025

Online detection of forecast model inadequacies using forecast errors

Thomas Grundy, Rebecca Killick, Ivan Svetunkov

In many organisations, accurate forecasts are essential for making informed decisions for a variety of applications from inventory management to staffing optimization. Whatever for…

stat.AP2023

A changepoint approach to modelling non-stationary soil moisture dynamics

Mengyi Gong, Rebecca Killick, Christopher Nemeth +1

Soil moisture dynamics provide an indicator of soil health that scientists model via drydown curves. The typical modelling process requires the soil moisture time series to be manu…

stat.ME2021★ 3 cited

A Comparison of Single and Multiple Changepoint Techniques for Time Series Data

Xueheng Shi, Colin Gallagher, Robert Lund +1

This paper describes and compares several prominent single and multiple changepoint techniques for time series data. Due to their importance in inferential matters, changepoint res…

stat.ME2020★ 31 cited

High-Dimensional Changepoint Detection via a Geometrically Inspired Mapping

Thomas Grundy, Rebecca Killick, Gueorgui Mihaylov

High-dimensional changepoint analysis is a growing area of research and has applications in a wide range of fields. The aim is to accurately and efficiently detect changepoints in…

stat.AP2019★ 7 cited

A Bayesian binary algorithm for RMS-based acoustic signal segmentation

Paulo Hubert, Rebecca Killick, Alexandra Chung +1

Changepoint analysis (also known as segmentation analysis) aims at analyzing an ordered, one-dimensional vector, in order to find locations where some characteristic of the data ch…