3 citations · 5 across the 6 of their papers we have counts for
6 papers
skchange: Fast and Flexible Algorithms for Changepoint Detection
Martin Tveten, Johannes Voll Kolstø, Per August Jarval Moen
Skchange is an open-source Python library for detecting structural changes in time series. It implements modern change detection algorithms within a unified and extensible framewor…
gridcp: Fast Online Changepoint Detection in Python
Per August Jarval Moen, Sebastian Grau Nielsen, Espen Bjørge Urheim +2
Online changepoint detection is the problem of detecting distributional changes in a data stream in real-time. A large body of methodology exists for the offline (fixed-size) setti…
Efficient sparsity adaptive changepoint estimation
Per August Jarval Moen, Ingrid Kristine Glad, Martin Tveten
We propose a new, computationally efficient, sparsity adaptive changepoint estimator for detecting changes in unknown subsets of a high-dimensional data sequence. Assuming the data…
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…
Online Detection of Sparse Changes in High-Dimensional Data Streams Using Tailored Projections
Martin Tveten, Ingrid K. Glad
When applying principal component analysis (PCA) for dimension reduction, the most varying projections are usually used in order to retain most of the information. For the purpose…
Which principal components are most sensitive to distributional changes?
Martin Tveten
PCA is often used in anomaly detection and statistical process control tasks. For bivariate data, we prove that the minor projection (the least varying projection) of the PCA-rotat…